{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.utils import np_utils\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(4)\n",
    "class CustomHistory(keras.callbacks.Callback):\n",
    "    def init(self):\n",
    "        self.train_loss=[]\n",
    "        self.val_loss=[]\n",
    "        self.train_acc=[]\n",
    "        self.val_acc=[]\n",
    "    def on_epoch_end(self,batch,logs={}):\n",
    "        self.train_loss.append(logs.get('loss'))\n",
    "        self.val_loss.append(logs.get('val_loss'))\n",
    "        self.train_acc.append(logs.get('accuracy'))\n",
    "        self.val_acc.append(logs.get('val_accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 375s 33us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val=x_train[50000:]\n",
    "y_val=y_train[50000:]\n",
    "x_train=x_train[:50000]\n",
    "y_train=y_train[:50000]\n",
    "x_train=x_train.reshape(50000,784).astype('float32')/255.0\n",
    "x_val=x_val.reshape(10000,784).astype('float32')/255.0\n",
    "x_test=x_test.reshape(10000,784).astype('float32')/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rand_idxs=np.random.choice(50000,700)\n",
    "val_rand_idxs=np.random.choice(10000,300)\n",
    "x_train=x_train[train_rand_idxs]\n",
    "y_train=y_train[train_rand_idxs]\n",
    "x_val=x_val[val_rand_idxs]\n",
    "y_val=y_val[val_rand_idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_val=np_utils.to_categorical(y_val)\n",
    "y_test=np_utils.to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(units=2,input_dim=28*28,activation='relu'))\n",
    "model.add(Dense(units=10,activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 0\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 1s 1ms/step - loss: 2.2878 - accuracy: 0.0986 - val_loss: 2.2644 - val_accuracy: 0.0933\n",
      "epochs: 1\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 275us/step - loss: 2.2515 - accuracy: 0.0986 - val_loss: 2.2339 - val_accuracy: 0.0900\n",
      "epochs: 2\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 2.2265 - accuracy: 0.1243 - val_loss: 2.2159 - val_accuracy: 0.1933\n",
      "epochs: 3\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 275us/step - loss: 2.2075 - accuracy: 0.1343 - val_loss: 2.2056 - val_accuracy: 0.2200\n",
      "epochs: 4\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 291us/step - loss: 2.1925 - accuracy: 0.1671 - val_loss: 2.1910 - val_accuracy: 0.2067\n",
      "epochs: 5\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 2.1764 - accuracy: 0.1800 - val_loss: 2.1802 - val_accuracy: 0.2133\n",
      "epochs: 6\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 277us/step - loss: 2.1633 - accuracy: 0.1843 - val_loss: 2.1652 - val_accuracy: 0.2100\n",
      "epochs: 7\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 2.1479 - accuracy: 0.2000 - val_loss: 2.1504 - val_accuracy: 0.1733\n",
      "epochs: 8\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 272us/step - loss: 2.1338 - accuracy: 0.1971 - val_loss: 2.1412 - val_accuracy: 0.2333\n",
      "epochs: 9\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 2.1200 - accuracy: 0.2186 - val_loss: 2.1259 - val_accuracy: 0.2200\n",
      "epochs: 10\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 2.1070 - accuracy: 0.2143 - val_loss: 2.1146 - val_accuracy: 0.2267\n",
      "epochs: 11\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 334us/step - loss: 2.0947 - accuracy: 0.2214 - val_loss: 2.1045 - val_accuracy: 0.2300\n",
      "epochs: 12\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 387us/step - loss: 2.0823 - accuracy: 0.2300 - val_loss: 2.0925 - val_accuracy: 0.2300\n",
      "epochs: 13\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 378us/step - loss: 2.0715 - accuracy: 0.2371 - val_loss: 2.0832 - val_accuracy: 0.2467\n",
      "epochs: 14\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 376us/step - loss: 2.0611 - accuracy: 0.2400 - val_loss: 2.0751 - val_accuracy: 0.2633\n",
      "epochs: 15\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 306us/step - loss: 2.0510 - accuracy: 0.2486 - val_loss: 2.0666 - val_accuracy: 0.2667\n",
      "epochs: 16\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 281us/step - loss: 2.0412 - accuracy: 0.2571 - val_loss: 2.0582 - val_accuracy: 0.2767\n",
      "epochs: 17\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 295us/step - loss: 2.0319 - accuracy: 0.2629 - val_loss: 2.0503 - val_accuracy: 0.2633\n",
      "epochs: 18\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 305us/step - loss: 2.0246 - accuracy: 0.2729 - val_loss: 2.0437 - val_accuracy: 0.2867\n",
      "epochs: 19\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 2.0163 - accuracy: 0.2857 - val_loss: 2.0369 - val_accuracy: 0.2933\n",
      "epochs: 20\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 2.0085 - accuracy: 0.2914 - val_loss: 2.0313 - val_accuracy: 0.2900\n",
      "epochs: 21\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 274us/step - loss: 2.0011 - accuracy: 0.2900 - val_loss: 2.0238 - val_accuracy: 0.2967\n",
      "epochs: 22\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 277us/step - loss: 1.9937 - accuracy: 0.2900 - val_loss: 2.0206 - val_accuracy: 0.2967\n",
      "epochs: 23\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 298us/step - loss: 1.9874 - accuracy: 0.2943 - val_loss: 2.0141 - val_accuracy: 0.2967\n",
      "epochs: 24\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 318us/step - loss: 1.9801 - accuracy: 0.2914 - val_loss: 2.0085 - val_accuracy: 0.3000\n",
      "epochs: 25\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 310us/step - loss: 1.9737 - accuracy: 0.2971 - val_loss: 2.0013 - val_accuracy: 0.2967\n",
      "epochs: 26\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 276us/step - loss: 1.9658 - accuracy: 0.3014 - val_loss: 1.9951 - val_accuracy: 0.2933\n",
      "epochs: 27\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 280us/step - loss: 1.9595 - accuracy: 0.2971 - val_loss: 1.9901 - val_accuracy: 0.3033\n",
      "epochs: 28\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 286us/step - loss: 1.9505 - accuracy: 0.3029 - val_loss: 1.9840 - val_accuracy: 0.3033\n",
      "epochs: 29\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 290us/step - loss: 1.9429 - accuracy: 0.3000 - val_loss: 1.9776 - val_accuracy: 0.3067\n",
      "epochs: 30\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 281us/step - loss: 1.9325 - accuracy: 0.3071 - val_loss: 1.9724 - val_accuracy: 0.3100\n",
      "epochs: 31\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 285us/step - loss: 1.9215 - accuracy: 0.2986 - val_loss: 1.9658 - val_accuracy: 0.3133\n",
      "epochs: 32\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 275us/step - loss: 1.9111 - accuracy: 0.3071 - val_loss: 1.9555 - val_accuracy: 0.3067\n",
      "epochs: 33\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 287us/step - loss: 1.8972 - accuracy: 0.3057 - val_loss: 1.9461 - val_accuracy: 0.3300\n",
      "epochs: 34\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 319us/step - loss: 1.8831 - accuracy: 0.3243 - val_loss: 1.9335 - val_accuracy: 0.3533\n",
      "epochs: 35\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 309us/step - loss: 1.8655 - accuracy: 0.3514 - val_loss: 1.9197 - val_accuracy: 0.3800\n",
      "epochs: 36\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 310us/step - loss: 1.8454 - accuracy: 0.3729 - val_loss: 1.9045 - val_accuracy: 0.3900\n",
      "epochs: 37\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 307us/step - loss: 1.8229 - accuracy: 0.3857 - val_loss: 1.8876 - val_accuracy: 0.3867\n",
      "epochs: 38\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 291us/step - loss: 1.8016 - accuracy: 0.3586 - val_loss: 1.8735 - val_accuracy: 0.3767\n",
      "epochs: 39\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 282us/step - loss: 1.7768 - accuracy: 0.3714 - val_loss: 1.8621 - val_accuracy: 0.3467\n",
      "epochs: 40\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 309us/step - loss: 1.7520 - accuracy: 0.3671 - val_loss: 1.8472 - val_accuracy: 0.3533\n",
      "epochs: 41\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 302us/step - loss: 1.7310 - accuracy: 0.3714 - val_loss: 1.8310 - val_accuracy: 0.3467\n",
      "epochs: 42\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 321us/step - loss: 1.7079 - accuracy: 0.3700 - val_loss: 1.8117 - val_accuracy: 0.3867\n",
      "epochs: 43\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 276us/step - loss: 1.6889 - accuracy: 0.3786 - val_loss: 1.8085 - val_accuracy: 0.3500\n",
      "epochs: 44\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 277us/step - loss: 1.6725 - accuracy: 0.3814 - val_loss: 1.7908 - val_accuracy: 0.3833\n",
      "epochs: 45\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 1.6543 - accuracy: 0.4014 - val_loss: 1.7800 - val_accuracy: 0.3967\n",
      "epochs: 46\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.6384 - accuracy: 0.4186 - val_loss: 1.7685 - val_accuracy: 0.3967\n",
      "epochs: 47\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 296us/step - loss: 1.6232 - accuracy: 0.4329 - val_loss: 1.7603 - val_accuracy: 0.3967\n",
      "epochs: 48\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 1.6082 - accuracy: 0.4371 - val_loss: 1.7520 - val_accuracy: 0.3900\n",
      "epochs: 49\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.5933 - accuracy: 0.4500 - val_loss: 1.7448 - val_accuracy: 0.4200\n",
      "epochs: 50\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 281us/step - loss: 1.5784 - accuracy: 0.4714 - val_loss: 1.7278 - val_accuracy: 0.4067\n",
      "epochs: 51\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 274us/step - loss: 1.5654 - accuracy: 0.4700 - val_loss: 1.7177 - val_accuracy: 0.4267\n",
      "epochs: 52\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.5524 - accuracy: 0.4857 - val_loss: 1.7113 - val_accuracy: 0.4333\n",
      "epochs: 53\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 287us/step - loss: 1.5377 - accuracy: 0.4843 - val_loss: 1.7063 - val_accuracy: 0.4233\n",
      "epochs: 54\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 274us/step - loss: 1.5267 - accuracy: 0.4871 - val_loss: 1.6983 - val_accuracy: 0.4367\n",
      "epochs: 55\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 270us/step - loss: 1.5139 - accuracy: 0.5043 - val_loss: 1.7016 - val_accuracy: 0.4200\n",
      "epochs: 56\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 283us/step - loss: 1.5028 - accuracy: 0.4829 - val_loss: 1.6791 - val_accuracy: 0.4467\n",
      "epochs: 57\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 272us/step - loss: 1.4927 - accuracy: 0.5029 - val_loss: 1.6792 - val_accuracy: 0.4567\n",
      "epochs: 58\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 281us/step - loss: 1.4812 - accuracy: 0.5043 - val_loss: 1.6687 - val_accuracy: 0.4367\n",
      "epochs: 59\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 274us/step - loss: 1.4711 - accuracy: 0.4957 - val_loss: 1.6670 - val_accuracy: 0.4233\n",
      "epochs: 60\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 1.4601 - accuracy: 0.5129 - val_loss: 1.6793 - val_accuracy: 0.4333\n",
      "epochs: 61\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 276us/step - loss: 1.4508 - accuracy: 0.5143 - val_loss: 1.6535 - val_accuracy: 0.4500\n",
      "epochs: 62\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 1.4410 - accuracy: 0.5071 - val_loss: 1.6502 - val_accuracy: 0.4733\n",
      "epochs: 63\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 283us/step - loss: 1.4307 - accuracy: 0.5143 - val_loss: 1.6477 - val_accuracy: 0.4733\n",
      "epochs: 64\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 282us/step - loss: 1.4245 - accuracy: 0.5229 - val_loss: 1.6435 - val_accuracy: 0.4533\n",
      "epochs: 65\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 284us/step - loss: 1.4137 - accuracy: 0.5200 - val_loss: 1.6353 - val_accuracy: 0.4900\n",
      "epochs: 66\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 277us/step - loss: 1.4053 - accuracy: 0.5314 - val_loss: 1.6307 - val_accuracy: 0.4733\n",
      "epochs: 67\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 1.3959 - accuracy: 0.5343 - val_loss: 1.6279 - val_accuracy: 0.4767\n",
      "epochs: 68\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 276us/step - loss: 1.3896 - accuracy: 0.5157 - val_loss: 1.6195 - val_accuracy: 0.4700\n",
      "epochs: 69\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 283us/step - loss: 1.3805 - accuracy: 0.5371 - val_loss: 1.6202 - val_accuracy: 0.4767\n",
      "epochs: 70\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 1.3732 - accuracy: 0.5386 - val_loss: 1.6149 - val_accuracy: 0.4767\n",
      "epochs: 71\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.3639 - accuracy: 0.5471 - val_loss: 1.6221 - val_accuracy: 0.4333\n",
      "epochs: 72\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 267us/step - loss: 1.3573 - accuracy: 0.5371 - val_loss: 1.6073 - val_accuracy: 0.5067\n",
      "epochs: 73\n",
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      "700/700 [==============================] - 0s 285us/step - loss: 1.3521 - accuracy: 0.5314 - val_loss: 1.6106 - val_accuracy: 0.4700\n",
      "epochs: 74\n",
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      "700/700 [==============================] - 0s 287us/step - loss: 1.3432 - accuracy: 0.5529 - val_loss: 1.5990 - val_accuracy: 0.4833\n",
      "epochs: 75\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.3371 - accuracy: 0.5486 - val_loss: 1.6058 - val_accuracy: 0.4667\n",
      "epochs: 76\n",
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      "700/700 [==============================] - 0s 275us/step - loss: 1.3280 - accuracy: 0.5543 - val_loss: 1.5976 - val_accuracy: 0.4600\n",
      "epochs: 77\n",
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      "700/700 [==============================] - 0s 285us/step - loss: 1.3235 - accuracy: 0.5543 - val_loss: 1.6113 - val_accuracy: 0.4567\n",
      "epochs: 78\n",
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      "700/700 [==============================] - 0s 282us/step - loss: 1.3180 - accuracy: 0.5600 - val_loss: 1.5990 - val_accuracy: 0.4833\n",
      "epochs: 79\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.3112 - accuracy: 0.5529 - val_loss: 1.6006 - val_accuracy: 0.4633\n",
      "epochs: 80\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.3061 - accuracy: 0.5571 - val_loss: 1.5941 - val_accuracy: 0.4700\n",
      "epochs: 81\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.3002 - accuracy: 0.5757 - val_loss: 1.5993 - val_accuracy: 0.4600\n",
      "epochs: 82\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 276us/step - loss: 1.2946 - accuracy: 0.5529 - val_loss: 1.5883 - val_accuracy: 0.4733\n",
      "epochs: 83\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.2851 - accuracy: 0.5729 - val_loss: 1.6018 - val_accuracy: 0.4633\n",
      "epochs: 84\n",
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      "700/700 [==============================] - 0s 278us/step - loss: 1.2808 - accuracy: 0.5729 - val_loss: 1.5832 - val_accuracy: 0.4800\n",
      "epochs: 85\n",
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      "700/700 [==============================] - 0s 272us/step - loss: 1.2766 - accuracy: 0.5671 - val_loss: 1.5878 - val_accuracy: 0.4767\n",
      "epochs: 86\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 1.2698 - accuracy: 0.5786 - val_loss: 1.6001 - val_accuracy: 0.4433\n",
      "epochs: 87\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.2659 - accuracy: 0.5857 - val_loss: 1.5876 - val_accuracy: 0.4900\n",
      "epochs: 88\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.2593 - accuracy: 0.5729 - val_loss: 1.5799 - val_accuracy: 0.4733\n",
      "epochs: 89\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 293us/step - loss: 1.2568 - accuracy: 0.5643 - val_loss: 1.5884 - val_accuracy: 0.4733\n",
      "epochs: 90\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 425us/step - loss: 1.2512 - accuracy: 0.5786 - val_loss: 1.5862 - val_accuracy: 0.4733\n",
      "epochs: 91\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 332us/step - loss: 1.2477 - accuracy: 0.5743 - val_loss: 1.5941 - val_accuracy: 0.4567\n",
      "epochs: 92\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 328us/step - loss: 1.2409 - accuracy: 0.5857 - val_loss: 1.5904 - val_accuracy: 0.4567\n",
      "epochs: 93\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 302us/step - loss: 1.2405 - accuracy: 0.5786 - val_loss: 1.5915 - val_accuracy: 0.4700\n",
      "epochs: 94\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.2341 - accuracy: 0.5929 - val_loss: 1.5839 - val_accuracy: 0.4767\n",
      "epochs: 95\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.2295 - accuracy: 0.5914 - val_loss: 1.5863 - val_accuracy: 0.4767\n",
      "epochs: 96\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.2264 - accuracy: 0.5843 - val_loss: 1.5877 - val_accuracy: 0.4800\n",
      "epochs: 97\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.2205 - accuracy: 0.5929 - val_loss: 1.5815 - val_accuracy: 0.4733\n",
      "epochs: 98\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 282us/step - loss: 1.2154 - accuracy: 0.5843 - val_loss: 1.5933 - val_accuracy: 0.4767\n",
      "epochs: 99\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 270us/step - loss: 1.2122 - accuracy: 0.5800 - val_loss: 1.5890 - val_accuracy: 0.4800\n",
      "epochs: 100\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 1.2059 - accuracy: 0.6100 - val_loss: 1.5829 - val_accuracy: 0.4800\n",
      "epochs: 101\n",
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      "700/700 [==============================] - 0s 266us/step - loss: 1.2048 - accuracy: 0.5800 - val_loss: 1.5918 - val_accuracy: 0.4900\n",
      "epochs: 102\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.2013 - accuracy: 0.5814 - val_loss: 1.5934 - val_accuracy: 0.4833\n",
      "epochs: 103\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.1952 - accuracy: 0.5986 - val_loss: 1.6109 - val_accuracy: 0.4700\n",
      "epochs: 104\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.1953 - accuracy: 0.6071 - val_loss: 1.5932 - val_accuracy: 0.4833\n",
      "epochs: 105\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 279us/step - loss: 1.1883 - accuracy: 0.5943 - val_loss: 1.5922 - val_accuracy: 0.4800\n",
      "epochs: 106\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.1872 - accuracy: 0.6057 - val_loss: 1.5842 - val_accuracy: 0.4767\n",
      "epochs: 107\n",
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      "700/700 [==============================] - 0s 313us/step - loss: 1.1818 - accuracy: 0.6071 - val_loss: 1.5866 - val_accuracy: 0.4700\n",
      "epochs: 108\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.1772 - accuracy: 0.6057 - val_loss: 1.5933 - val_accuracy: 0.4667\n",
      "epochs: 109\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.1734 - accuracy: 0.6157 - val_loss: 1.5924 - val_accuracy: 0.4800\n",
      "epochs: 110\n",
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      "700/700 [==============================] - 0s 274us/step - loss: 1.1726 - accuracy: 0.6071 - val_loss: 1.5946 - val_accuracy: 0.4733\n",
      "epochs: 111\n",
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      "700/700 [==============================] - 0s 272us/step - loss: 1.1685 - accuracy: 0.6000 - val_loss: 1.5902 - val_accuracy: 0.4733\n",
      "epochs: 112\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.1639 - accuracy: 0.6071 - val_loss: 1.6015 - val_accuracy: 0.4767\n",
      "epochs: 113\n",
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      "700/700 [==============================] - 0s 280us/step - loss: 1.1617 - accuracy: 0.6014 - val_loss: 1.5957 - val_accuracy: 0.4733\n",
      "epochs: 114\n",
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      "700/700 [==============================] - 0s 282us/step - loss: 1.1590 - accuracy: 0.6043 - val_loss: 1.6015 - val_accuracy: 0.4833\n",
      "epochs: 115\n",
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      "700/700 [==============================] - 0s 296us/step - loss: 1.1551 - accuracy: 0.6186 - val_loss: 1.6064 - val_accuracy: 0.4733\n",
      "epochs: 116\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.1525 - accuracy: 0.6186 - val_loss: 1.6035 - val_accuracy: 0.4700\n",
      "epochs: 117\n",
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      "700/700 [==============================] - 0s 270us/step - loss: 1.1450 - accuracy: 0.5943 - val_loss: 1.6001 - val_accuracy: 0.4700\n",
      "epochs: 118\n",
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      "700/700 [==============================] - 0s 284us/step - loss: 1.1468 - accuracy: 0.6100 - val_loss: 1.6089 - val_accuracy: 0.4633\n",
      "epochs: 119\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.1413 - accuracy: 0.6043 - val_loss: 1.5975 - val_accuracy: 0.4900\n",
      "epochs: 120\n",
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      "700/700 [==============================] - 0s 275us/step - loss: 1.1384 - accuracy: 0.6143 - val_loss: 1.6015 - val_accuracy: 0.4767\n",
      "epochs: 121\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 1.1371 - accuracy: 0.6114 - val_loss: 1.6000 - val_accuracy: 0.4967\n",
      "epochs: 122\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.1333 - accuracy: 0.6043 - val_loss: 1.5973 - val_accuracy: 0.4767\n",
      "epochs: 123\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 287us/step - loss: 1.1299 - accuracy: 0.6157 - val_loss: 1.6055 - val_accuracy: 0.4867\n",
      "epochs: 124\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.1266 - accuracy: 0.6186 - val_loss: 1.6020 - val_accuracy: 0.5033\n",
      "epochs: 125\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 279us/step - loss: 1.1240 - accuracy: 0.6057 - val_loss: 1.5974 - val_accuracy: 0.5067\n",
      "epochs: 126\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 271us/step - loss: 1.1183 - accuracy: 0.6143 - val_loss: 1.6055 - val_accuracy: 0.4733\n",
      "epochs: 127\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 286us/step - loss: 1.1160 - accuracy: 0.6343 - val_loss: 1.6069 - val_accuracy: 0.4867\n",
      "epochs: 128\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 266us/step - loss: 1.1157 - accuracy: 0.6171 - val_loss: 1.6081 - val_accuracy: 0.5000\n",
      "epochs: 129\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 283us/step - loss: 1.1104 - accuracy: 0.6229 - val_loss: 1.6073 - val_accuracy: 0.4933\n",
      "epochs: 130\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 1.1063 - accuracy: 0.6343 - val_loss: 1.6144 - val_accuracy: 0.5067\n",
      "epochs: 131\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.1059 - accuracy: 0.6229 - val_loss: 1.6093 - val_accuracy: 0.4900\n",
      "epochs: 132\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 274us/step - loss: 1.1032 - accuracy: 0.6300 - val_loss: 1.6474 - val_accuracy: 0.4633\n",
      "epochs: 133\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.0989 - accuracy: 0.6357 - val_loss: 1.6339 - val_accuracy: 0.4833\n",
      "epochs: 134\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 279us/step - loss: 1.0973 - accuracy: 0.6243 - val_loss: 1.6079 - val_accuracy: 0.5067\n",
      "epochs: 135\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 274us/step - loss: 1.0941 - accuracy: 0.6300 - val_loss: 1.6169 - val_accuracy: 0.4800\n",
      "epochs: 136\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 271us/step - loss: 1.0909 - accuracy: 0.6271 - val_loss: 1.6142 - val_accuracy: 0.5033\n",
      "epochs: 137\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 271us/step - loss: 1.0868 - accuracy: 0.6386 - val_loss: 1.6361 - val_accuracy: 0.4967\n",
      "epochs: 138\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 268us/step - loss: 1.0850 - accuracy: 0.6386 - val_loss: 1.6296 - val_accuracy: 0.5033\n",
      "epochs: 139\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 269us/step - loss: 1.0849 - accuracy: 0.6314 - val_loss: 1.6280 - val_accuracy: 0.5033\n",
      "epochs: 140\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 1.0793 - accuracy: 0.6371 - val_loss: 1.6368 - val_accuracy: 0.4867\n",
      "epochs: 141\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 275us/step - loss: 1.0781 - accuracy: 0.6271 - val_loss: 1.6283 - val_accuracy: 0.5100\n",
      "epochs: 142\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.0764 - accuracy: 0.6314 - val_loss: 1.6290 - val_accuracy: 0.5067\n",
      "epochs: 143\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.0728 - accuracy: 0.6414 - val_loss: 1.6320 - val_accuracy: 0.5000\n",
      "epochs: 144\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 286us/step - loss: 1.0662 - accuracy: 0.6357 - val_loss: 1.6604 - val_accuracy: 0.4800\n",
      "epochs: 145\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 278us/step - loss: 1.0670 - accuracy: 0.6357 - val_loss: 1.6300 - val_accuracy: 0.4967\n",
      "epochs: 146\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 1.0643 - accuracy: 0.6257 - val_loss: 1.6280 - val_accuracy: 0.5033\n",
      "epochs: 147\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 272us/step - loss: 1.0631 - accuracy: 0.6457 - val_loss: 1.6377 - val_accuracy: 0.4833\n",
      "epochs: 148\n",
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      "700/700 [==============================] - 0s 272us/step - loss: 1.0589 - accuracy: 0.6443 - val_loss: 1.6554 - val_accuracy: 0.4767\n",
      "epochs: 149\n",
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      "700/700 [==============================] - 0s 270us/step - loss: 1.0577 - accuracy: 0.6400 - val_loss: 1.6495 - val_accuracy: 0.4933\n",
      "epochs: 150\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.0558 - accuracy: 0.6457 - val_loss: 1.6410 - val_accuracy: 0.4600\n",
      "epochs: 151\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.0538 - accuracy: 0.6486 - val_loss: 1.6469 - val_accuracy: 0.4767\n",
      "epochs: 152\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.0510 - accuracy: 0.6429 - val_loss: 1.6513 - val_accuracy: 0.4833\n",
      "epochs: 153\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 281us/step - loss: 1.0481 - accuracy: 0.6471 - val_loss: 1.6528 - val_accuracy: 0.4933\n",
      "epochs: 154\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.0452 - accuracy: 0.6400 - val_loss: 1.6624 - val_accuracy: 0.5000\n",
      "epochs: 155\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 277us/step - loss: 1.0441 - accuracy: 0.6486 - val_loss: 1.6454 - val_accuracy: 0.5100\n",
      "epochs: 156\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 1.0390 - accuracy: 0.6529 - val_loss: 1.6519 - val_accuracy: 0.5000\n",
      "epochs: 157\n",
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      "700/700 [==============================] - 0s 266us/step - loss: 1.0378 - accuracy: 0.6514 - val_loss: 1.6532 - val_accuracy: 0.4800\n",
      "epochs: 158\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 1.0380 - accuracy: 0.6471 - val_loss: 1.6705 - val_accuracy: 0.4933\n",
      "epochs: 159\n",
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      "700/700 [==============================] - 0s 280us/step - loss: 1.0332 - accuracy: 0.6471 - val_loss: 1.6580 - val_accuracy: 0.4967\n",
      "epochs: 160\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 1.0329 - accuracy: 0.6586 - val_loss: 1.6561 - val_accuracy: 0.4933\n",
      "epochs: 161\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.0294 - accuracy: 0.6543 - val_loss: 1.6689 - val_accuracy: 0.5000\n",
      "epochs: 162\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 272us/step - loss: 1.0290 - accuracy: 0.6557 - val_loss: 1.6817 - val_accuracy: 0.4833\n",
      "epochs: 163\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 1.0248 - accuracy: 0.6543 - val_loss: 1.6634 - val_accuracy: 0.4867\n",
      "epochs: 164\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 269us/step - loss: 1.0268 - accuracy: 0.6543 - val_loss: 1.6679 - val_accuracy: 0.4800\n",
      "epochs: 165\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 1.0221 - accuracy: 0.6586 - val_loss: 1.6729 - val_accuracy: 0.4733\n",
      "epochs: 166\n",
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      "700/700 [==============================] - 0s 288us/step - loss: 1.0206 - accuracy: 0.6471 - val_loss: 1.6684 - val_accuracy: 0.4833\n",
      "epochs: 167\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 1.0174 - accuracy: 0.6414 - val_loss: 1.6771 - val_accuracy: 0.4867\n",
      "epochs: 168\n",
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      "700/700 [==============================] - 0s 263us/step - loss: 1.0156 - accuracy: 0.6600 - val_loss: 1.6669 - val_accuracy: 0.4833\n",
      "epochs: 169\n",
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      "700/700 [==============================] - 0s 264us/step - loss: 1.0123 - accuracy: 0.6557 - val_loss: 1.6754 - val_accuracy: 0.4933\n",
      "epochs: 170\n",
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      "700/700 [==============================] - 0s 305us/step - loss: 1.0134 - accuracy: 0.6514 - val_loss: 1.6736 - val_accuracy: 0.4767\n",
      "epochs: 171\n",
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      "700/700 [==============================] - 0s 346us/step - loss: 1.0102 - accuracy: 0.6586 - val_loss: 1.6780 - val_accuracy: 0.4900\n",
      "epochs: 172\n",
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      "700/700 [==============================] - 0s 331us/step - loss: 1.0054 - accuracy: 0.6614 - val_loss: 1.7071 - val_accuracy: 0.4667\n",
      "epochs: 173\n",
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      "700/700 [==============================] - 0s 343us/step - loss: 1.0038 - accuracy: 0.6614 - val_loss: 1.6946 - val_accuracy: 0.4733\n",
      "epochs: 174\n",
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      "700/700 [==============================] - 0s 278us/step - loss: 1.0026 - accuracy: 0.6557 - val_loss: 1.6941 - val_accuracy: 0.4833\n",
      "epochs: 175\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 277us/step - loss: 0.9990 - accuracy: 0.6571 - val_loss: 1.6986 - val_accuracy: 0.4900\n",
      "epochs: 176\n",
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      "700/700 [==============================] - 0s 269us/step - loss: 0.9988 - accuracy: 0.6657 - val_loss: 1.6909 - val_accuracy: 0.4867\n",
      "epochs: 177\n",
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      "700/700 [==============================] - 0s 266us/step - loss: 0.9956 - accuracy: 0.6657 - val_loss: 1.7007 - val_accuracy: 0.4567\n",
      "epochs: 178\n",
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      "700/700 [==============================] - 0s 268us/step - loss: 0.9966 - accuracy: 0.6557 - val_loss: 1.6858 - val_accuracy: 0.4833\n",
      "epochs: 179\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 270us/step - loss: 0.9924 - accuracy: 0.6614 - val_loss: 1.6894 - val_accuracy: 0.4800\n",
      "epochs: 180\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 285us/step - loss: 0.9928 - accuracy: 0.6629 - val_loss: 1.7027 - val_accuracy: 0.4700\n",
      "epochs: 181\n",
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      "700/700 [==============================] - 0s 282us/step - loss: 0.9883 - accuracy: 0.6657 - val_loss: 1.6961 - val_accuracy: 0.4967\n",
      "epochs: 182\n",
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      "700/700 [==============================] - 0s 265us/step - loss: 0.9878 - accuracy: 0.6657 - val_loss: 1.7117 - val_accuracy: 0.4833\n",
      "epochs: 183\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 273us/step - loss: 0.9856 - accuracy: 0.6629 - val_loss: 1.7179 - val_accuracy: 0.4833\n",
      "epochs: 184\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 278us/step - loss: 0.9828 - accuracy: 0.6743 - val_loss: 1.7092 - val_accuracy: 0.4767\n",
      "epochs: 185\n",
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      "700/700 [==============================] - 0s 279us/step - loss: 0.9810 - accuracy: 0.6700 - val_loss: 1.7015 - val_accuracy: 0.4833\n",
      "epochs: 186\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 271us/step - loss: 0.9785 - accuracy: 0.6614 - val_loss: 1.7042 - val_accuracy: 0.4800\n",
      "epochs: 187\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 266us/step - loss: 0.9788 - accuracy: 0.6686 - val_loss: 1.7054 - val_accuracy: 0.4900\n",
      "epochs: 188\n",
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      "700/700 [==============================] - 0s 273us/step - loss: 0.9753 - accuracy: 0.6614 - val_loss: 1.7311 - val_accuracy: 0.4633\n",
      "epochs: 189\n",
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      "700/700 [==============================] - 0s 271us/step - loss: 0.9736 - accuracy: 0.6629 - val_loss: 1.7212 - val_accuracy: 0.4900\n",
      "epochs: 190\n",
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      "700/700 [==============================] - 0s 276us/step - loss: 0.9732 - accuracy: 0.6629 - val_loss: 1.7094 - val_accuracy: 0.4800\n",
      "epochs: 191\n",
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      "700/700 [==============================] - 0s 279us/step - loss: 0.9707 - accuracy: 0.6714 - val_loss: 1.7327 - val_accuracy: 0.4800\n",
      "epochs: 192\n",
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      "700/700 [==============================] - 0s 284us/step - loss: 0.9700 - accuracy: 0.6671 - val_loss: 1.7366 - val_accuracy: 0.4700\n",
      "epochs: 193\n",
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      "700/700 [==============================] - 0s 279us/step - loss: 0.9683 - accuracy: 0.6700 - val_loss: 1.7291 - val_accuracy: 0.4700\n",
      "epochs: 194\n",
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      "700/700 [==============================] - 0s 288us/step - loss: 0.9646 - accuracy: 0.6829 - val_loss: 1.7557 - val_accuracy: 0.4700\n",
      "epochs: 195\n",
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      "700/700 [==============================] - 0s 304us/step - loss: 0.9640 - accuracy: 0.6729 - val_loss: 1.7373 - val_accuracy: 0.4833\n",
      "epochs: 196\n",
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      "700/700 [==============================] - 0s 324us/step - loss: 0.9641 - accuracy: 0.6729 - val_loss: 1.7285 - val_accuracy: 0.4800\n",
      "epochs: 197\n",
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      "700/700 [==============================] - 0s 334us/step - loss: 0.9599 - accuracy: 0.6786 - val_loss: 1.7444 - val_accuracy: 0.4600\n",
      "epochs: 198\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 357us/step - loss: 0.9568 - accuracy: 0.6786 - val_loss: 1.7416 - val_accuracy: 0.4767\n",
      "epochs: 199\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 366us/step - loss: 0.9589 - accuracy: 0.6729 - val_loss: 1.7302 - val_accuracy: 0.4700\n",
      "epochs: 200\n",
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      "700/700 [==============================] - 0s 367us/step - loss: 0.9527 - accuracy: 0.6671 - val_loss: 1.7373 - val_accuracy: 0.4767\n",
      "epochs: 201\n",
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      "700/700 [==============================] - 0s 369us/step - loss: 0.9534 - accuracy: 0.6757 - val_loss: 1.7453 - val_accuracy: 0.4567\n",
      "epochs: 202\n",
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      "700/700 [==============================] - 0s 371us/step - loss: 0.9550 - accuracy: 0.6771 - val_loss: 1.7506 - val_accuracy: 0.4833\n",
      "epochs: 203\n",
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      "700/700 [==============================] - 0s 384us/step - loss: 0.9498 - accuracy: 0.6786 - val_loss: 1.7596 - val_accuracy: 0.4633\n",
      "epochs: 204\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 383us/step - loss: 0.9488 - accuracy: 0.6743 - val_loss: 1.7559 - val_accuracy: 0.4767\n",
      "epochs: 205\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 387us/step - loss: 0.9481 - accuracy: 0.6843 - val_loss: 1.7522 - val_accuracy: 0.4667\n",
      "epochs: 206\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 376us/step - loss: 0.9438 - accuracy: 0.6857 - val_loss: 1.7682 - val_accuracy: 0.4800\n",
      "epochs: 207\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 381us/step - loss: 0.9443 - accuracy: 0.6829 - val_loss: 1.7707 - val_accuracy: 0.4867\n",
      "epochs: 208\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 381us/step - loss: 0.9439 - accuracy: 0.6800 - val_loss: 1.7507 - val_accuracy: 0.4900\n",
      "epochs: 209\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.9422 - accuracy: 0.6771 - val_loss: 1.7603 - val_accuracy: 0.4767\n",
      "epochs: 210\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.9383 - accuracy: 0.6829 - val_loss: 1.7681 - val_accuracy: 0.4733\n",
      "epochs: 211\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 438us/step - loss: 0.9373 - accuracy: 0.6857 - val_loss: 1.7748 - val_accuracy: 0.4933\n",
      "epochs: 212\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.9356 - accuracy: 0.6743 - val_loss: 1.7623 - val_accuracy: 0.4933\n",
      "epochs: 213\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 391us/step - loss: 0.9356 - accuracy: 0.6843 - val_loss: 1.7758 - val_accuracy: 0.4800\n",
      "epochs: 214\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 411us/step - loss: 0.9330 - accuracy: 0.6886 - val_loss: 1.7692 - val_accuracy: 0.4733\n",
      "epochs: 215\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.9303 - accuracy: 0.6871 - val_loss: 1.7763 - val_accuracy: 0.4833\n",
      "epochs: 216\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 397us/step - loss: 0.9278 - accuracy: 0.6829 - val_loss: 1.7757 - val_accuracy: 0.4800\n",
      "epochs: 217\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 456us/step - loss: 0.9269 - accuracy: 0.6786 - val_loss: 1.7829 - val_accuracy: 0.4933\n",
      "epochs: 218\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 419us/step - loss: 0.9267 - accuracy: 0.6829 - val_loss: 1.7866 - val_accuracy: 0.4767\n",
      "epochs: 219\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 374us/step - loss: 0.9259 - accuracy: 0.6886 - val_loss: 1.7816 - val_accuracy: 0.4900\n",
      "epochs: 220\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 410us/step - loss: 0.9240 - accuracy: 0.6871 - val_loss: 1.7964 - val_accuracy: 0.4833\n",
      "epochs: 221\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 413us/step - loss: 0.9214 - accuracy: 0.6857 - val_loss: 1.7971 - val_accuracy: 0.4700\n",
      "epochs: 222\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.9196 - accuracy: 0.6814 - val_loss: 1.8008 - val_accuracy: 0.5000\n",
      "epochs: 223\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 378us/step - loss: 0.9192 - accuracy: 0.6814 - val_loss: 1.7940 - val_accuracy: 0.4900\n",
      "epochs: 224\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 397us/step - loss: 0.9150 - accuracy: 0.6900 - val_loss: 1.7873 - val_accuracy: 0.4733\n",
      "epochs: 225\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 420us/step - loss: 0.9156 - accuracy: 0.6957 - val_loss: 1.7903 - val_accuracy: 0.4800\n",
      "epochs: 226\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 389us/step - loss: 0.9153 - accuracy: 0.6886 - val_loss: 1.7898 - val_accuracy: 0.4867\n",
      "epochs: 227\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 397us/step - loss: 0.9119 - accuracy: 0.6900 - val_loss: 1.8010 - val_accuracy: 0.4867\n",
      "epochs: 228\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 415us/step - loss: 0.9108 - accuracy: 0.6871 - val_loss: 1.7958 - val_accuracy: 0.4767\n",
      "epochs: 229\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 426us/step - loss: 0.9112 - accuracy: 0.7000 - val_loss: 1.8161 - val_accuracy: 0.4667\n",
      "epochs: 230\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.9078 - accuracy: 0.6829 - val_loss: 1.7973 - val_accuracy: 0.4800\n",
      "epochs: 231\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 408us/step - loss: 0.9076 - accuracy: 0.6871 - val_loss: 1.8141 - val_accuracy: 0.5000\n",
      "epochs: 232\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 429us/step - loss: 0.9048 - accuracy: 0.6986 - val_loss: 1.8020 - val_accuracy: 0.4833\n",
      "epochs: 233\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 372us/step - loss: 0.9058 - accuracy: 0.6957 - val_loss: 1.8231 - val_accuracy: 0.4833\n",
      "epochs: 234\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.9045 - accuracy: 0.6900 - val_loss: 1.8372 - val_accuracy: 0.4833\n",
      "epochs: 235\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 478us/step - loss: 0.9012 - accuracy: 0.6914 - val_loss: 1.8136 - val_accuracy: 0.4800\n",
      "epochs: 236\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.8993 - accuracy: 0.6871 - val_loss: 1.8144 - val_accuracy: 0.4800\n",
      "epochs: 237\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 415us/step - loss: 0.8980 - accuracy: 0.6857 - val_loss: 1.8328 - val_accuracy: 0.4667\n",
      "epochs: 238\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 419us/step - loss: 0.8975 - accuracy: 0.7014 - val_loss: 1.8119 - val_accuracy: 0.4833\n",
      "epochs: 239\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 426us/step - loss: 0.8965 - accuracy: 0.6957 - val_loss: 1.8179 - val_accuracy: 0.4833\n",
      "epochs: 240\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.8955 - accuracy: 0.6986 - val_loss: 1.8209 - val_accuracy: 0.4933\n",
      "epochs: 241\n",
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      "700/700 [==============================] - 0s 413us/step - loss: 0.8945 - accuracy: 0.7086 - val_loss: 1.8344 - val_accuracy: 0.4767\n",
      "epochs: 242\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 424us/step - loss: 0.8919 - accuracy: 0.6871 - val_loss: 1.8452 - val_accuracy: 0.4700\n",
      "epochs: 243\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.8896 - accuracy: 0.6943 - val_loss: 1.8322 - val_accuracy: 0.4800\n",
      "epochs: 244\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 401us/step - loss: 0.8894 - accuracy: 0.6971 - val_loss: 1.8273 - val_accuracy: 0.4833\n",
      "epochs: 245\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.8875 - accuracy: 0.6971 - val_loss: 1.8411 - val_accuracy: 0.4933\n",
      "epochs: 246\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 380us/step - loss: 0.8873 - accuracy: 0.7000 - val_loss: 1.8505 - val_accuracy: 0.4633\n",
      "epochs: 247\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 373us/step - loss: 0.8865 - accuracy: 0.7029 - val_loss: 1.8526 - val_accuracy: 0.4733\n",
      "epochs: 248\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 359us/step - loss: 0.8847 - accuracy: 0.7071 - val_loss: 1.8504 - val_accuracy: 0.4867\n",
      "epochs: 249\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 369us/step - loss: 0.8828 - accuracy: 0.6986 - val_loss: 1.8920 - val_accuracy: 0.4567\n",
      "epochs: 250\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 337us/step - loss: 0.8777 - accuracy: 0.7157 - val_loss: 1.8793 - val_accuracy: 0.4900\n",
      "epochs: 251\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 359us/step - loss: 0.8804 - accuracy: 0.7000 - val_loss: 1.8510 - val_accuracy: 0.4733\n",
      "epochs: 252\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 352us/step - loss: 0.8789 - accuracy: 0.7071 - val_loss: 1.8570 - val_accuracy: 0.4700\n",
      "epochs: 253\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 381us/step - loss: 0.8779 - accuracy: 0.6971 - val_loss: 1.8569 - val_accuracy: 0.4767\n",
      "epochs: 254\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 339us/step - loss: 0.8764 - accuracy: 0.6971 - val_loss: 1.8622 - val_accuracy: 0.4700\n",
      "epochs: 255\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 363us/step - loss: 0.8748 - accuracy: 0.7029 - val_loss: 1.8764 - val_accuracy: 0.4833\n",
      "epochs: 256\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 395us/step - loss: 0.8736 - accuracy: 0.7014 - val_loss: 1.8807 - val_accuracy: 0.4533\n",
      "epochs: 257\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 360us/step - loss: 0.8733 - accuracy: 0.7114 - val_loss: 1.8863 - val_accuracy: 0.4733\n",
      "epochs: 258\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 361us/step - loss: 0.8735 - accuracy: 0.7071 - val_loss: 1.8664 - val_accuracy: 0.4667\n",
      "epochs: 259\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 362us/step - loss: 0.8689 - accuracy: 0.7000 - val_loss: 1.8781 - val_accuracy: 0.4700\n",
      "epochs: 260\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 344us/step - loss: 0.8678 - accuracy: 0.7029 - val_loss: 1.8960 - val_accuracy: 0.4667\n",
      "epochs: 261\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 369us/step - loss: 0.8687 - accuracy: 0.7071 - val_loss: 1.8675 - val_accuracy: 0.4833\n",
      "epochs: 262\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 333us/step - loss: 0.8678 - accuracy: 0.7143 - val_loss: 1.8989 - val_accuracy: 0.4600\n",
      "epochs: 263\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 346us/step - loss: 0.8652 - accuracy: 0.7029 - val_loss: 1.8934 - val_accuracy: 0.4800\n",
      "epochs: 264\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 352us/step - loss: 0.8640 - accuracy: 0.7071 - val_loss: 1.9042 - val_accuracy: 0.4667\n",
      "epochs: 265\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 376us/step - loss: 0.8622 - accuracy: 0.7071 - val_loss: 1.8902 - val_accuracy: 0.4900\n",
      "epochs: 266\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 364us/step - loss: 0.8632 - accuracy: 0.7000 - val_loss: 1.8745 - val_accuracy: 0.4667\n",
      "epochs: 267\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 355us/step - loss: 0.8625 - accuracy: 0.7014 - val_loss: 1.8906 - val_accuracy: 0.4767\n",
      "epochs: 268\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 359us/step - loss: 0.8585 - accuracy: 0.7086 - val_loss: 1.9174 - val_accuracy: 0.4700\n",
      "epochs: 269\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - ETA: 0s - loss: 0.8419 - accuracy: 0.71 - 0s 371us/step - loss: 0.8596 - accuracy: 0.7057 - val_loss: 1.9084 - val_accuracy: 0.4700\n",
      "epochs: 270\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 347us/step - loss: 0.8568 - accuracy: 0.7186 - val_loss: 1.9069 - val_accuracy: 0.4633\n",
      "epochs: 271\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 335us/step - loss: 0.8579 - accuracy: 0.7086 - val_loss: 1.8963 - val_accuracy: 0.4900\n",
      "epochs: 272\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 339us/step - loss: 0.8564 - accuracy: 0.7129 - val_loss: 1.9224 - val_accuracy: 0.4700\n",
      "epochs: 273\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 369us/step - loss: 0.8532 - accuracy: 0.7143 - val_loss: 1.8875 - val_accuracy: 0.4700\n",
      "epochs: 274\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 356us/step - loss: 0.8522 - accuracy: 0.7171 - val_loss: 1.9215 - val_accuracy: 0.4767\n",
      "epochs: 275\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 362us/step - loss: 0.8516 - accuracy: 0.7029 - val_loss: 1.9056 - val_accuracy: 0.4733\n",
      "epochs: 276\n",
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      "700/700 [==============================] - 0s 358us/step - loss: 0.8528 - accuracy: 0.7114 - val_loss: 1.9107 - val_accuracy: 0.4967\n",
      "epochs: 277\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 410us/step - loss: 0.8503 - accuracy: 0.7143 - val_loss: 1.9165 - val_accuracy: 0.4600\n",
      "epochs: 278\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 385us/step - loss: 0.8486 - accuracy: 0.7086 - val_loss: 1.9183 - val_accuracy: 0.4700\n",
      "epochs: 279\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.8453 - accuracy: 0.7100 - val_loss: 1.9201 - val_accuracy: 0.4900\n",
      "epochs: 280\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 431us/step - loss: 0.8464 - accuracy: 0.7171 - val_loss: 1.9145 - val_accuracy: 0.4900\n",
      "epochs: 281\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.8439 - accuracy: 0.7157 - val_loss: 1.9271 - val_accuracy: 0.4800\n",
      "epochs: 282\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.8466 - accuracy: 0.7129 - val_loss: 1.9196 - val_accuracy: 0.4633\n",
      "epochs: 283\n",
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      "700/700 [==============================] - 0s 466us/step - loss: 0.8433 - accuracy: 0.7071 - val_loss: 1.9138 - val_accuracy: 0.4667\n",
      "epochs: 284\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 431us/step - loss: 0.8431 - accuracy: 0.7129 - val_loss: 1.9225 - val_accuracy: 0.4733\n",
      "epochs: 285\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.8402 - accuracy: 0.7200 - val_loss: 1.9384 - val_accuracy: 0.4900\n",
      "epochs: 286\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.8378 - accuracy: 0.7100 - val_loss: 1.9303 - val_accuracy: 0.4700\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "epochs: 287\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 422us/step - loss: 0.8392 - accuracy: 0.7171 - val_loss: 1.9457 - val_accuracy: 0.4733\n",
      "epochs: 288\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.8387 - accuracy: 0.7157 - val_loss: 1.9419 - val_accuracy: 0.4800\n",
      "epochs: 289\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 440us/step - loss: 0.8363 - accuracy: 0.7157 - val_loss: 1.9415 - val_accuracy: 0.4633\n",
      "epochs: 290\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.8362 - accuracy: 0.7243 - val_loss: 1.9265 - val_accuracy: 0.4733\n",
      "epochs: 291\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.8344 - accuracy: 0.7229 - val_loss: 1.9392 - val_accuracy: 0.4733\n",
      "epochs: 292\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.8332 - accuracy: 0.7186 - val_loss: 1.9428 - val_accuracy: 0.4800\n",
      "epochs: 293\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.8346 - accuracy: 0.7271 - val_loss: 1.9509 - val_accuracy: 0.4767\n",
      "epochs: 294\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 509us/step - loss: 0.8314 - accuracy: 0.7186 - val_loss: 1.9561 - val_accuracy: 0.4667\n",
      "epochs: 295\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 411us/step - loss: 0.8313 - accuracy: 0.7157 - val_loss: 1.9439 - val_accuracy: 0.4800\n",
      "epochs: 296\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.8304 - accuracy: 0.7243 - val_loss: 1.9548 - val_accuracy: 0.4833\n",
      "epochs: 297\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 428us/step - loss: 0.8278 - accuracy: 0.7314 - val_loss: 1.9813 - val_accuracy: 0.4767\n",
      "epochs: 298\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 407us/step - loss: 0.8275 - accuracy: 0.7171 - val_loss: 1.9833 - val_accuracy: 0.4633\n",
      "epochs: 299\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.8281 - accuracy: 0.7171 - val_loss: 1.9501 - val_accuracy: 0.4767\n",
      "epochs: 300\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 388us/step - loss: 0.8271 - accuracy: 0.7186 - val_loss: 1.9559 - val_accuracy: 0.4767\n",
      "epochs: 301\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 367us/step - loss: 0.8255 - accuracy: 0.7243 - val_loss: 1.9758 - val_accuracy: 0.4833\n",
      "epochs: 302\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 338us/step - loss: 0.8237 - accuracy: 0.7243 - val_loss: 1.9841 - val_accuracy: 0.4700\n",
      "epochs: 303\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 361us/step - loss: 0.8251 - accuracy: 0.7186 - val_loss: 1.9719 - val_accuracy: 0.4733\n",
      "epochs: 304\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 379us/step - loss: 0.8212 - accuracy: 0.7314 - val_loss: 1.9753 - val_accuracy: 0.4567\n",
      "epochs: 305\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 331us/step - loss: 0.8216 - accuracy: 0.7229 - val_loss: 1.9689 - val_accuracy: 0.4700\n",
      "epochs: 306\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 339us/step - loss: 0.8194 - accuracy: 0.7229 - val_loss: 1.9759 - val_accuracy: 0.4600\n",
      "epochs: 307\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 373us/step - loss: 0.8192 - accuracy: 0.7300 - val_loss: 2.0016 - val_accuracy: 0.4733\n",
      "epochs: 308\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 349us/step - loss: 0.8174 - accuracy: 0.7243 - val_loss: 1.9815 - val_accuracy: 0.4833\n",
      "epochs: 309\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 344us/step - loss: 0.8164 - accuracy: 0.7200 - val_loss: 1.9851 - val_accuracy: 0.4733\n",
      "epochs: 310\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 379us/step - loss: 0.8180 - accuracy: 0.7271 - val_loss: 1.9884 - val_accuracy: 0.4600\n",
      "epochs: 311\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 347us/step - loss: 0.8157 - accuracy: 0.7343 - val_loss: 2.0147 - val_accuracy: 0.4767\n",
      "epochs: 312\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 372us/step - loss: 0.8156 - accuracy: 0.7314 - val_loss: 2.0254 - val_accuracy: 0.4700\n",
      "epochs: 313\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 343us/step - loss: 0.8166 - accuracy: 0.7271 - val_loss: 2.0082 - val_accuracy: 0.4767\n",
      "epochs: 314\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 372us/step - loss: 0.8127 - accuracy: 0.7314 - val_loss: 2.0163 - val_accuracy: 0.4800\n",
      "epochs: 315\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 347us/step - loss: 0.8125 - accuracy: 0.7314 - val_loss: 2.0056 - val_accuracy: 0.4867\n",
      "epochs: 316\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 369us/step - loss: 0.8115 - accuracy: 0.7300 - val_loss: 2.0069 - val_accuracy: 0.4667\n",
      "epochs: 317\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 352us/step - loss: 0.8102 - accuracy: 0.7257 - val_loss: 2.0159 - val_accuracy: 0.4833\n",
      "epochs: 318\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 363us/step - loss: 0.8082 - accuracy: 0.7286 - val_loss: 2.0234 - val_accuracy: 0.4667\n",
      "epochs: 319\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 334us/step - loss: 0.8078 - accuracy: 0.7286 - val_loss: 2.0175 - val_accuracy: 0.4667\n",
      "epochs: 320\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 380us/step - loss: 0.8064 - accuracy: 0.7329 - val_loss: 2.0134 - val_accuracy: 0.4733\n",
      "epochs: 321\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 367us/step - loss: 0.8068 - accuracy: 0.7243 - val_loss: 2.0137 - val_accuracy: 0.4733\n",
      "epochs: 322\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 338us/step - loss: 0.8057 - accuracy: 0.7300 - val_loss: 1.9950 - val_accuracy: 0.4667\n",
      "epochs: 323\n",
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      "700/700 [==============================] - 0s 365us/step - loss: 0.8047 - accuracy: 0.7300 - val_loss: 1.9990 - val_accuracy: 0.4633\n",
      "epochs: 324\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 375us/step - loss: 0.8041 - accuracy: 0.7300 - val_loss: 2.0273 - val_accuracy: 0.4700\n",
      "epochs: 325\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 351us/step - loss: 0.8029 - accuracy: 0.7300 - val_loss: 2.0249 - val_accuracy: 0.4667\n",
      "epochs: 326\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 396us/step - loss: 0.8001 - accuracy: 0.7229 - val_loss: 2.0315 - val_accuracy: 0.4900\n",
      "epochs: 327\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 351us/step - loss: 0.8005 - accuracy: 0.7271 - val_loss: 2.0436 - val_accuracy: 0.4767\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "epochs: 328\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 365us/step - loss: 0.7997 - accuracy: 0.7357 - val_loss: 2.0447 - val_accuracy: 0.4700\n",
      "epochs: 329\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 359us/step - loss: 0.7972 - accuracy: 0.7414 - val_loss: 2.0753 - val_accuracy: 0.4633\n",
      "epochs: 330\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 357us/step - loss: 0.7968 - accuracy: 0.7314 - val_loss: 2.0205 - val_accuracy: 0.4633\n",
      "epochs: 331\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 359us/step - loss: 0.7954 - accuracy: 0.7429 - val_loss: 2.0691 - val_accuracy: 0.4700\n",
      "epochs: 332\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 361us/step - loss: 0.7978 - accuracy: 0.7243 - val_loss: 2.0604 - val_accuracy: 0.4633\n",
      "epochs: 333\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 362us/step - loss: 0.7963 - accuracy: 0.7300 - val_loss: 2.0498 - val_accuracy: 0.4600\n",
      "epochs: 334\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 360us/step - loss: 0.7944 - accuracy: 0.7414 - val_loss: 2.0442 - val_accuracy: 0.4767\n",
      "epochs: 335\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 388us/step - loss: 0.7941 - accuracy: 0.7257 - val_loss: 2.0537 - val_accuracy: 0.4633\n",
      "epochs: 336\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 340us/step - loss: 0.7941 - accuracy: 0.7343 - val_loss: 2.0689 - val_accuracy: 0.4733\n",
      "epochs: 337\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 360us/step - loss: 0.7897 - accuracy: 0.7300 - val_loss: 2.0539 - val_accuracy: 0.4667\n",
      "epochs: 338\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 372us/step - loss: 0.7893 - accuracy: 0.7300 - val_loss: 2.0288 - val_accuracy: 0.4667\n",
      "epochs: 339\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 399us/step - loss: 0.7921 - accuracy: 0.7271 - val_loss: 2.0505 - val_accuracy: 0.4633\n",
      "epochs: 340\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 384us/step - loss: 0.7900 - accuracy: 0.7329 - val_loss: 2.0519 - val_accuracy: 0.4767\n",
      "epochs: 341\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 397us/step - loss: 0.7865 - accuracy: 0.7371 - val_loss: 2.0539 - val_accuracy: 0.4733\n",
      "epochs: 342\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 394us/step - loss: 0.7887 - accuracy: 0.7429 - val_loss: 2.0739 - val_accuracy: 0.4567\n",
      "epochs: 343\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.7887 - accuracy: 0.7329 - val_loss: 2.0766 - val_accuracy: 0.4667\n",
      "epochs: 344\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7868 - accuracy: 0.7329 - val_loss: 2.0663 - val_accuracy: 0.4700\n",
      "epochs: 345\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 422us/step - loss: 0.7869 - accuracy: 0.7371 - val_loss: 2.0835 - val_accuracy: 0.4667\n",
      "epochs: 346\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 401us/step - loss: 0.7852 - accuracy: 0.7471 - val_loss: 2.0968 - val_accuracy: 0.4700\n",
      "epochs: 347\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 431us/step - loss: 0.7839 - accuracy: 0.7286 - val_loss: 2.0669 - val_accuracy: 0.4733\n",
      "epochs: 348\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 408us/step - loss: 0.7843 - accuracy: 0.7457 - val_loss: 2.0863 - val_accuracy: 0.4733\n",
      "epochs: 349\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 385us/step - loss: 0.7829 - accuracy: 0.7329 - val_loss: 2.0988 - val_accuracy: 0.4733\n",
      "epochs: 350\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 469us/step - loss: 0.7828 - accuracy: 0.7329 - val_loss: 2.1009 - val_accuracy: 0.4767\n",
      "epochs: 351\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 493us/step - loss: 0.7786 - accuracy: 0.7414 - val_loss: 2.0959 - val_accuracy: 0.4700\n",
      "epochs: 352\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 487us/step - loss: 0.7810 - accuracy: 0.7500 - val_loss: 2.0717 - val_accuracy: 0.4700\n",
      "epochs: 353\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.7810 - accuracy: 0.7471 - val_loss: 2.0824 - val_accuracy: 0.4733\n",
      "epochs: 354\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 457us/step - loss: 0.7781 - accuracy: 0.7457 - val_loss: 2.1030 - val_accuracy: 0.4767\n",
      "epochs: 355\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.7776 - accuracy: 0.7471 - val_loss: 2.0978 - val_accuracy: 0.4867\n",
      "epochs: 356\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.7770 - accuracy: 0.7414 - val_loss: 2.1207 - val_accuracy: 0.4667\n",
      "epochs: 357\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.7776 - accuracy: 0.7414 - val_loss: 2.0880 - val_accuracy: 0.4700\n",
      "epochs: 358\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 416us/step - loss: 0.7763 - accuracy: 0.7386 - val_loss: 2.1086 - val_accuracy: 0.4767\n",
      "epochs: 359\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.7750 - accuracy: 0.7386 - val_loss: 2.1089 - val_accuracy: 0.4700\n",
      "epochs: 360\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.7732 - accuracy: 0.7443 - val_loss: 2.1040 - val_accuracy: 0.4667\n",
      "epochs: 361\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 438us/step - loss: 0.7733 - accuracy: 0.7443 - val_loss: 2.1201 - val_accuracy: 0.4633\n",
      "epochs: 362\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.7718 - accuracy: 0.7429 - val_loss: 2.0890 - val_accuracy: 0.4700\n",
      "epochs: 363\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.7715 - accuracy: 0.7343 - val_loss: 2.1253 - val_accuracy: 0.4733\n",
      "epochs: 364\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.7717 - accuracy: 0.7414 - val_loss: 2.1363 - val_accuracy: 0.4633\n",
      "epochs: 365\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 450us/step - loss: 0.7705 - accuracy: 0.7414 - val_loss: 2.1148 - val_accuracy: 0.4733\n",
      "epochs: 366\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7687 - accuracy: 0.7443 - val_loss: 2.1315 - val_accuracy: 0.4733\n",
      "epochs: 367\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.7689 - accuracy: 0.7429 - val_loss: 2.1355 - val_accuracy: 0.4800\n",
      "epochs: 368\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.7691 - accuracy: 0.7443 - val_loss: 2.1278 - val_accuracy: 0.4833\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 369\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.7683 - accuracy: 0.7471 - val_loss: 2.1397 - val_accuracy: 0.4767\n",
      "epochs: 370\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.7640 - accuracy: 0.7414 - val_loss: 2.1450 - val_accuracy: 0.4667\n",
      "epochs: 371\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 437us/step - loss: 0.7676 - accuracy: 0.7443 - val_loss: 2.1228 - val_accuracy: 0.4767\n",
      "epochs: 372\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 421us/step - loss: 0.7656 - accuracy: 0.7371 - val_loss: 2.1218 - val_accuracy: 0.4800\n",
      "epochs: 373\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 423us/step - loss: 0.7657 - accuracy: 0.7514 - val_loss: 2.1396 - val_accuracy: 0.4800\n",
      "epochs: 374\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.7621 - accuracy: 0.7443 - val_loss: 2.1321 - val_accuracy: 0.4800\n",
      "epochs: 375\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7623 - accuracy: 0.7429 - val_loss: 2.1374 - val_accuracy: 0.4667\n",
      "epochs: 376\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7632 - accuracy: 0.7443 - val_loss: 2.1475 - val_accuracy: 0.4800\n",
      "epochs: 377\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 426us/step - loss: 0.7619 - accuracy: 0.7443 - val_loss: 2.1625 - val_accuracy: 0.4667\n",
      "epochs: 378\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 419us/step - loss: 0.7607 - accuracy: 0.7471 - val_loss: 2.1330 - val_accuracy: 0.4733\n",
      "epochs: 379\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 459us/step - loss: 0.7586 - accuracy: 0.7500 - val_loss: 2.1675 - val_accuracy: 0.4600\n",
      "epochs: 380\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7594 - accuracy: 0.7443 - val_loss: 2.1393 - val_accuracy: 0.4800\n",
      "epochs: 381\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 445us/step - loss: 0.7573 - accuracy: 0.7500 - val_loss: 2.1799 - val_accuracy: 0.4700\n",
      "epochs: 382\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.7582 - accuracy: 0.7514 - val_loss: 2.1554 - val_accuracy: 0.4800\n",
      "epochs: 383\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.7543 - accuracy: 0.7529 - val_loss: 2.1538 - val_accuracy: 0.4833\n",
      "epochs: 384\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 416us/step - loss: 0.7571 - accuracy: 0.7457 - val_loss: 2.1689 - val_accuracy: 0.4733\n",
      "epochs: 385\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 416us/step - loss: 0.7555 - accuracy: 0.7486 - val_loss: 2.1964 - val_accuracy: 0.4633\n",
      "epochs: 386\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.7533 - accuracy: 0.7500 - val_loss: 2.1813 - val_accuracy: 0.4867\n",
      "epochs: 387\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 421us/step - loss: 0.7547 - accuracy: 0.7486 - val_loss: 2.1810 - val_accuracy: 0.4667\n",
      "epochs: 388\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7522 - accuracy: 0.7457 - val_loss: 2.1622 - val_accuracy: 0.4700\n",
      "epochs: 389\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.7507 - accuracy: 0.7471 - val_loss: 2.1617 - val_accuracy: 0.4800\n",
      "epochs: 390\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 406us/step - loss: 0.7512 - accuracy: 0.7471 - val_loss: 2.1553 - val_accuracy: 0.4700\n",
      "epochs: 391\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 420us/step - loss: 0.7515 - accuracy: 0.7514 - val_loss: 2.1919 - val_accuracy: 0.4633\n",
      "epochs: 392\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.7515 - accuracy: 0.7486 - val_loss: 2.1867 - val_accuracy: 0.4767\n",
      "epochs: 393\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 413us/step - loss: 0.7495 - accuracy: 0.7514 - val_loss: 2.1830 - val_accuracy: 0.4667\n",
      "epochs: 394\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 429us/step - loss: 0.7483 - accuracy: 0.7486 - val_loss: 2.2114 - val_accuracy: 0.4567\n",
      "epochs: 395\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.7492 - accuracy: 0.7429 - val_loss: 2.1879 - val_accuracy: 0.4900\n",
      "epochs: 396\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.7478 - accuracy: 0.7457 - val_loss: 2.2048 - val_accuracy: 0.4667\n",
      "epochs: 397\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.7465 - accuracy: 0.7443 - val_loss: 2.1741 - val_accuracy: 0.4700\n",
      "epochs: 398\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 428us/step - loss: 0.7463 - accuracy: 0.7529 - val_loss: 2.2176 - val_accuracy: 0.4733\n",
      "epochs: 399\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.7452 - accuracy: 0.7500 - val_loss: 2.2318 - val_accuracy: 0.4567\n",
      "epochs: 400\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 402us/step - loss: 0.7450 - accuracy: 0.7557 - val_loss: 2.2070 - val_accuracy: 0.4700\n",
      "epochs: 401\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 457us/step - loss: 0.7433 - accuracy: 0.7471 - val_loss: 2.1929 - val_accuracy: 0.4600\n",
      "epochs: 402\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 523us/step - loss: 0.7432 - accuracy: 0.7557 - val_loss: 2.2053 - val_accuracy: 0.4800\n",
      "epochs: 403\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 537us/step - loss: 0.7410 - accuracy: 0.7500 - val_loss: 2.2070 - val_accuracy: 0.4600\n",
      "epochs: 404\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 505us/step - loss: 0.7442 - accuracy: 0.7543 - val_loss: 2.2098 - val_accuracy: 0.4633\n",
      "epochs: 405\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 446us/step - loss: 0.7415 - accuracy: 0.7457 - val_loss: 2.2232 - val_accuracy: 0.4767\n",
      "epochs: 406\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 419us/step - loss: 0.7412 - accuracy: 0.7500 - val_loss: 2.2035 - val_accuracy: 0.4767\n",
      "epochs: 407\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.7409 - accuracy: 0.7500 - val_loss: 2.2563 - val_accuracy: 0.4567\n",
      "epochs: 408\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.7423 - accuracy: 0.7543 - val_loss: 2.2317 - val_accuracy: 0.4667\n",
      "epochs: 409\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 415us/step - loss: 0.7382 - accuracy: 0.7600 - val_loss: 2.2465 - val_accuracy: 0.4700\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 410\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 466us/step - loss: 0.7393 - accuracy: 0.7543 - val_loss: 2.2390 - val_accuracy: 0.4500\n",
      "epochs: 411\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 417us/step - loss: 0.7394 - accuracy: 0.7529 - val_loss: 2.2227 - val_accuracy: 0.4633\n",
      "epochs: 412\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 420us/step - loss: 0.7373 - accuracy: 0.7529 - val_loss: 2.2205 - val_accuracy: 0.4733\n",
      "epochs: 413\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 438us/step - loss: 0.7344 - accuracy: 0.7586 - val_loss: 2.2336 - val_accuracy: 0.4633\n",
      "epochs: 414\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7366 - accuracy: 0.7514 - val_loss: 2.2397 - val_accuracy: 0.4800\n",
      "epochs: 415\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.7353 - accuracy: 0.7557 - val_loss: 2.2517 - val_accuracy: 0.4533\n",
      "epochs: 416\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.7362 - accuracy: 0.7543 - val_loss: 2.2635 - val_accuracy: 0.4600\n",
      "epochs: 417\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 433us/step - loss: 0.7330 - accuracy: 0.7557 - val_loss: 2.2475 - val_accuracy: 0.4767\n",
      "epochs: 418\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.7346 - accuracy: 0.7614 - val_loss: 2.2606 - val_accuracy: 0.4600\n",
      "epochs: 419\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 426us/step - loss: 0.7329 - accuracy: 0.7529 - val_loss: 2.2620 - val_accuracy: 0.4667\n",
      "epochs: 420\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7325 - accuracy: 0.7614 - val_loss: 2.2573 - val_accuracy: 0.4600\n",
      "epochs: 421\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.7296 - accuracy: 0.7543 - val_loss: 2.2509 - val_accuracy: 0.4700\n",
      "epochs: 422\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.7323 - accuracy: 0.7571 - val_loss: 2.2477 - val_accuracy: 0.4733\n",
      "epochs: 423\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 422us/step - loss: 0.7293 - accuracy: 0.7600 - val_loss: 2.2652 - val_accuracy: 0.4633\n",
      "epochs: 424\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 439us/step - loss: 0.7287 - accuracy: 0.7614 - val_loss: 2.2366 - val_accuracy: 0.4667\n",
      "epochs: 425\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.7299 - accuracy: 0.7557 - val_loss: 2.2766 - val_accuracy: 0.4433\n",
      "epochs: 426\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 425us/step - loss: 0.7291 - accuracy: 0.7586 - val_loss: 2.2542 - val_accuracy: 0.4700\n",
      "epochs: 427\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.7258 - accuracy: 0.7643 - val_loss: 2.2653 - val_accuracy: 0.4567\n",
      "epochs: 428\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.7290 - accuracy: 0.7614 - val_loss: 2.2732 - val_accuracy: 0.4667\n",
      "epochs: 429\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 424us/step - loss: 0.7251 - accuracy: 0.7643 - val_loss: 2.2913 - val_accuracy: 0.4767\n",
      "epochs: 430\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 423us/step - loss: 0.7268 - accuracy: 0.7514 - val_loss: 2.2864 - val_accuracy: 0.4767\n",
      "epochs: 431\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 410us/step - loss: 0.7245 - accuracy: 0.7643 - val_loss: 2.2837 - val_accuracy: 0.4733\n",
      "epochs: 432\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 402us/step - loss: 0.7255 - accuracy: 0.7586 - val_loss: 2.2971 - val_accuracy: 0.4733\n",
      "epochs: 433\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 450us/step - loss: 0.7240 - accuracy: 0.7600 - val_loss: 2.2744 - val_accuracy: 0.4567\n",
      "epochs: 434\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 466us/step - loss: 0.7228 - accuracy: 0.7643 - val_loss: 2.2924 - val_accuracy: 0.4633\n",
      "epochs: 435\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.7233 - accuracy: 0.7657 - val_loss: 2.3209 - val_accuracy: 0.4900\n",
      "epochs: 436\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 413us/step - loss: 0.7234 - accuracy: 0.7600 - val_loss: 2.2805 - val_accuracy: 0.4733\n",
      "epochs: 437\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 407us/step - loss: 0.7223 - accuracy: 0.7614 - val_loss: 2.2813 - val_accuracy: 0.4600\n",
      "epochs: 438\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 457us/step - loss: 0.7208 - accuracy: 0.7671 - val_loss: 2.2999 - val_accuracy: 0.4567\n",
      "epochs: 439\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 416us/step - loss: 0.7188 - accuracy: 0.7643 - val_loss: 2.3036 - val_accuracy: 0.4600\n",
      "epochs: 440\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.7205 - accuracy: 0.7657 - val_loss: 2.3044 - val_accuracy: 0.4633\n",
      "epochs: 441\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.7199 - accuracy: 0.7614 - val_loss: 2.2786 - val_accuracy: 0.4667\n",
      "epochs: 442\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.7198 - accuracy: 0.7600 - val_loss: 2.2779 - val_accuracy: 0.4667\n",
      "epochs: 443\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.7184 - accuracy: 0.7686 - val_loss: 2.3042 - val_accuracy: 0.4767\n",
      "epochs: 444\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 445us/step - loss: 0.7167 - accuracy: 0.7671 - val_loss: 2.3170 - val_accuracy: 0.4633\n",
      "epochs: 445\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.7180 - accuracy: 0.7629 - val_loss: 2.3185 - val_accuracy: 0.4667\n",
      "epochs: 446\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 425us/step - loss: 0.7176 - accuracy: 0.7571 - val_loss: 2.2953 - val_accuracy: 0.4667\n",
      "epochs: 447\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.7168 - accuracy: 0.7600 - val_loss: 2.3037 - val_accuracy: 0.4667\n",
      "epochs: 448\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.7157 - accuracy: 0.7614 - val_loss: 2.3132 - val_accuracy: 0.4600\n",
      "epochs: 449\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 437us/step - loss: 0.7147 - accuracy: 0.7714 - val_loss: 2.3377 - val_accuracy: 0.4500\n",
      "epochs: 450\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7131 - accuracy: 0.7657 - val_loss: 2.3685 - val_accuracy: 0.4633\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 451\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.7138 - accuracy: 0.7671 - val_loss: 2.3183 - val_accuracy: 0.4700\n",
      "epochs: 452\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 463us/step - loss: 0.7144 - accuracy: 0.7629 - val_loss: 2.3225 - val_accuracy: 0.4567\n",
      "epochs: 453\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 529us/step - loss: 0.7125 - accuracy: 0.7671 - val_loss: 2.3040 - val_accuracy: 0.4633\n",
      "epochs: 454\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 540us/step - loss: 0.7116 - accuracy: 0.7671 - val_loss: 2.3238 - val_accuracy: 0.4567\n",
      "epochs: 455\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.7113 - accuracy: 0.7657 - val_loss: 2.3041 - val_accuracy: 0.4700\n",
      "epochs: 456\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 475us/step - loss: 0.7123 - accuracy: 0.7714 - val_loss: 2.3258 - val_accuracy: 0.4533\n",
      "epochs: 457\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 450us/step - loss: 0.7100 - accuracy: 0.7714 - val_loss: 2.3471 - val_accuracy: 0.4733\n",
      "epochs: 458\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 437us/step - loss: 0.7110 - accuracy: 0.7600 - val_loss: 2.3316 - val_accuracy: 0.4700\n",
      "epochs: 459\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7118 - accuracy: 0.7729 - val_loss: 2.3225 - val_accuracy: 0.4633\n",
      "epochs: 460\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 483us/step - loss: 0.7082 - accuracy: 0.7714 - val_loss: 2.3440 - val_accuracy: 0.4633\n",
      "epochs: 461\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.7092 - accuracy: 0.7714 - val_loss: 2.3365 - val_accuracy: 0.4767\n",
      "epochs: 462\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 459us/step - loss: 0.7095 - accuracy: 0.7700 - val_loss: 2.3569 - val_accuracy: 0.4667\n",
      "epochs: 463\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.7083 - accuracy: 0.7757 - val_loss: 2.3633 - val_accuracy: 0.4600\n",
      "epochs: 464\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.7066 - accuracy: 0.7686 - val_loss: 2.3360 - val_accuracy: 0.4700\n",
      "epochs: 465\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.7070 - accuracy: 0.7714 - val_loss: 2.3479 - val_accuracy: 0.4500\n",
      "epochs: 466\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.7051 - accuracy: 0.7757 - val_loss: 2.3407 - val_accuracy: 0.4733\n",
      "epochs: 467\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.7065 - accuracy: 0.7714 - val_loss: 2.3800 - val_accuracy: 0.4467\n",
      "epochs: 468\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 459us/step - loss: 0.7044 - accuracy: 0.7714 - val_loss: 2.3394 - val_accuracy: 0.4667\n",
      "epochs: 469\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.7062 - accuracy: 0.7657 - val_loss: 2.3685 - val_accuracy: 0.4567\n",
      "epochs: 470\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.7034 - accuracy: 0.7657 - val_loss: 2.3531 - val_accuracy: 0.4633\n",
      "epochs: 471\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 413us/step - loss: 0.7027 - accuracy: 0.7786 - val_loss: 2.3901 - val_accuracy: 0.4733\n",
      "epochs: 472\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 487us/step - loss: 0.7029 - accuracy: 0.7729 - val_loss: 2.3653 - val_accuracy: 0.4600\n",
      "epochs: 473\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.7012 - accuracy: 0.7814 - val_loss: 2.3668 - val_accuracy: 0.4600\n",
      "epochs: 474\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.7017 - accuracy: 0.7729 - val_loss: 2.3833 - val_accuracy: 0.4633\n",
      "epochs: 475\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.7009 - accuracy: 0.7686 - val_loss: 2.3701 - val_accuracy: 0.4767\n",
      "epochs: 476\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 419us/step - loss: 0.7002 - accuracy: 0.7714 - val_loss: 2.3418 - val_accuracy: 0.4500\n",
      "epochs: 477\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6999 - accuracy: 0.7757 - val_loss: 2.4150 - val_accuracy: 0.4633\n",
      "epochs: 478\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.7001 - accuracy: 0.7771 - val_loss: 2.3893 - val_accuracy: 0.4633\n",
      "epochs: 479\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.7002 - accuracy: 0.7729 - val_loss: 2.3857 - val_accuracy: 0.4667\n",
      "epochs: 480\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 440us/step - loss: 0.6986 - accuracy: 0.7743 - val_loss: 2.3704 - val_accuracy: 0.4700\n",
      "epochs: 481\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 478us/step - loss: 0.6980 - accuracy: 0.7743 - val_loss: 2.3738 - val_accuracy: 0.4667\n",
      "epochs: 482\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 509us/step - loss: 0.6978 - accuracy: 0.7771 - val_loss: 2.4076 - val_accuracy: 0.4633\n",
      "epochs: 483\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 487us/step - loss: 0.6979 - accuracy: 0.7771 - val_loss: 2.3841 - val_accuracy: 0.4600\n",
      "epochs: 484\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.6976 - accuracy: 0.7786 - val_loss: 2.3937 - val_accuracy: 0.4667\n",
      "epochs: 485\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6958 - accuracy: 0.7729 - val_loss: 2.3641 - val_accuracy: 0.4533\n",
      "epochs: 486\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 464us/step - loss: 0.6958 - accuracy: 0.7729 - val_loss: 2.3943 - val_accuracy: 0.4733\n",
      "epochs: 487\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 471us/step - loss: 0.6932 - accuracy: 0.7729 - val_loss: 2.4383 - val_accuracy: 0.4533\n",
      "epochs: 488\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6956 - accuracy: 0.7786 - val_loss: 2.3861 - val_accuracy: 0.4600\n",
      "epochs: 489\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 445us/step - loss: 0.6951 - accuracy: 0.7757 - val_loss: 2.3865 - val_accuracy: 0.4700\n",
      "epochs: 490\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 457us/step - loss: 0.6946 - accuracy: 0.7743 - val_loss: 2.4258 - val_accuracy: 0.4567\n",
      "epochs: 491\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 458us/step - loss: 0.6948 - accuracy: 0.7786 - val_loss: 2.4468 - val_accuracy: 0.4733\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 492\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.6922 - accuracy: 0.7771 - val_loss: 2.4163 - val_accuracy: 0.4633\n",
      "epochs: 493\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.6919 - accuracy: 0.7800 - val_loss: 2.3957 - val_accuracy: 0.4733\n",
      "epochs: 494\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.6931 - accuracy: 0.7743 - val_loss: 2.4095 - val_accuracy: 0.4533\n",
      "epochs: 495\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.6908 - accuracy: 0.7757 - val_loss: 2.4233 - val_accuracy: 0.4600\n",
      "epochs: 496\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.6925 - accuracy: 0.7871 - val_loss: 2.4062 - val_accuracy: 0.4567\n",
      "epochs: 497\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 437us/step - loss: 0.6892 - accuracy: 0.7871 - val_loss: 2.4173 - val_accuracy: 0.4567\n",
      "epochs: 498\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.6895 - accuracy: 0.7771 - val_loss: 2.3976 - val_accuracy: 0.4633\n",
      "epochs: 499\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.6900 - accuracy: 0.7771 - val_loss: 2.4102 - val_accuracy: 0.4533\n",
      "epochs: 500\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.6906 - accuracy: 0.7771 - val_loss: 2.4210 - val_accuracy: 0.4600\n",
      "epochs: 501\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 466us/step - loss: 0.6903 - accuracy: 0.7843 - val_loss: 2.4559 - val_accuracy: 0.4800\n",
      "epochs: 502\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 520us/step - loss: 0.6877 - accuracy: 0.7771 - val_loss: 2.4470 - val_accuracy: 0.4633\n",
      "epochs: 503\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 530us/step - loss: 0.6871 - accuracy: 0.7800 - val_loss: 2.4177 - val_accuracy: 0.4600\n",
      "epochs: 504\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 512us/step - loss: 0.6875 - accuracy: 0.7857 - val_loss: 2.4381 - val_accuracy: 0.4700\n",
      "epochs: 505\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 472us/step - loss: 0.6869 - accuracy: 0.7800 - val_loss: 2.4714 - val_accuracy: 0.4633\n",
      "epochs: 506\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.6868 - accuracy: 0.7800 - val_loss: 2.4485 - val_accuracy: 0.4600\n",
      "epochs: 507\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.6867 - accuracy: 0.7800 - val_loss: 2.4426 - val_accuracy: 0.4600\n",
      "epochs: 508\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.6861 - accuracy: 0.7886 - val_loss: 2.4835 - val_accuracy: 0.4500\n",
      "epochs: 509\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 428us/step - loss: 0.6860 - accuracy: 0.7771 - val_loss: 2.4662 - val_accuracy: 0.4567\n",
      "epochs: 510\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6855 - accuracy: 0.7857 - val_loss: 2.4740 - val_accuracy: 0.4800\n",
      "epochs: 511\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.6849 - accuracy: 0.7757 - val_loss: 2.4425 - val_accuracy: 0.4567\n",
      "epochs: 512\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 439us/step - loss: 0.6840 - accuracy: 0.7857 - val_loss: 2.4373 - val_accuracy: 0.4667\n",
      "epochs: 513\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.6821 - accuracy: 0.7814 - val_loss: 2.4288 - val_accuracy: 0.4633\n",
      "epochs: 514\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.6843 - accuracy: 0.7843 - val_loss: 2.4511 - val_accuracy: 0.4600\n",
      "epochs: 515\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.6823 - accuracy: 0.7786 - val_loss: 2.4761 - val_accuracy: 0.4500\n",
      "epochs: 516\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.6819 - accuracy: 0.7843 - val_loss: 2.4769 - val_accuracy: 0.4667\n",
      "epochs: 517\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 427us/step - loss: 0.6827 - accuracy: 0.7843 - val_loss: 2.4629 - val_accuracy: 0.4700\n",
      "epochs: 518\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 436us/step - loss: 0.6818 - accuracy: 0.7800 - val_loss: 2.4817 - val_accuracy: 0.4700\n",
      "epochs: 519\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.6820 - accuracy: 0.7771 - val_loss: 2.4608 - val_accuracy: 0.4600\n",
      "epochs: 520\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6787 - accuracy: 0.7914 - val_loss: 2.4699 - val_accuracy: 0.4600\n",
      "epochs: 521\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6792 - accuracy: 0.7886 - val_loss: 2.4937 - val_accuracy: 0.4667\n",
      "epochs: 522\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.6796 - accuracy: 0.7857 - val_loss: 2.4543 - val_accuracy: 0.4700\n",
      "epochs: 523\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.6798 - accuracy: 0.7829 - val_loss: 2.4836 - val_accuracy: 0.4533\n",
      "epochs: 524\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6787 - accuracy: 0.7843 - val_loss: 2.4670 - val_accuracy: 0.4700\n",
      "epochs: 525\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.6800 - accuracy: 0.7786 - val_loss: 2.4751 - val_accuracy: 0.4733\n",
      "epochs: 526\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.6779 - accuracy: 0.7871 - val_loss: 2.4720 - val_accuracy: 0.4733\n",
      "epochs: 527\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 431us/step - loss: 0.6774 - accuracy: 0.7829 - val_loss: 2.4899 - val_accuracy: 0.4633\n",
      "epochs: 528\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.6766 - accuracy: 0.7886 - val_loss: 2.4693 - val_accuracy: 0.4600\n",
      "epochs: 529\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 478us/step - loss: 0.6755 - accuracy: 0.7843 - val_loss: 2.4702 - val_accuracy: 0.4633\n",
      "epochs: 530\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 418us/step - loss: 0.6767 - accuracy: 0.7843 - val_loss: 2.5049 - val_accuracy: 0.4633\n",
      "epochs: 531\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.6764 - accuracy: 0.7886 - val_loss: 2.4776 - val_accuracy: 0.4700\n",
      "epochs: 532\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.6761 - accuracy: 0.7914 - val_loss: 2.5008 - val_accuracy: 0.4667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 533\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.6740 - accuracy: 0.7857 - val_loss: 2.4972 - val_accuracy: 0.4600\n",
      "epochs: 534\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.6742 - accuracy: 0.7900 - val_loss: 2.4783 - val_accuracy: 0.4667\n",
      "epochs: 535\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 425us/step - loss: 0.6743 - accuracy: 0.7900 - val_loss: 2.4756 - val_accuracy: 0.4667\n",
      "epochs: 536\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 464us/step - loss: 0.6744 - accuracy: 0.7871 - val_loss: 2.5064 - val_accuracy: 0.4600\n",
      "epochs: 537\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 433us/step - loss: 0.6726 - accuracy: 0.7914 - val_loss: 2.5169 - val_accuracy: 0.4633\n",
      "epochs: 538\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.6724 - accuracy: 0.7886 - val_loss: 2.5062 - val_accuracy: 0.4700\n",
      "epochs: 539\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6714 - accuracy: 0.7900 - val_loss: 2.5026 - val_accuracy: 0.4700\n",
      "epochs: 540\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.6732 - accuracy: 0.7871 - val_loss: 2.5202 - val_accuracy: 0.4633\n",
      "epochs: 541\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.6718 - accuracy: 0.7914 - val_loss: 2.5038 - val_accuracy: 0.4700\n",
      "epochs: 542\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6707 - accuracy: 0.7914 - val_loss: 2.5199 - val_accuracy: 0.4700\n",
      "epochs: 543\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6720 - accuracy: 0.7929 - val_loss: 2.5013 - val_accuracy: 0.4700\n",
      "epochs: 544\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 426us/step - loss: 0.6689 - accuracy: 0.7929 - val_loss: 2.5240 - val_accuracy: 0.4467\n",
      "epochs: 545\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 464us/step - loss: 0.6698 - accuracy: 0.7914 - val_loss: 2.5028 - val_accuracy: 0.4667\n",
      "epochs: 546\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.6700 - accuracy: 0.7886 - val_loss: 2.5049 - val_accuracy: 0.4467\n",
      "epochs: 547\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 446us/step - loss: 0.6695 - accuracy: 0.7914 - val_loss: 2.5070 - val_accuracy: 0.4600\n",
      "epochs: 548\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.6679 - accuracy: 0.7943 - val_loss: 2.4907 - val_accuracy: 0.4633\n",
      "epochs: 549\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 463us/step - loss: 0.6672 - accuracy: 0.8000 - val_loss: 2.5369 - val_accuracy: 0.4667\n",
      "epochs: 550\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.6664 - accuracy: 0.7929 - val_loss: 2.5157 - val_accuracy: 0.4567\n",
      "epochs: 551\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 481us/step - loss: 0.6669 - accuracy: 0.7929 - val_loss: 2.5107 - val_accuracy: 0.4533\n",
      "epochs: 552\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 519us/step - loss: 0.6668 - accuracy: 0.7929 - val_loss: 2.5399 - val_accuracy: 0.4667\n",
      "epochs: 553\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 565us/step - loss: 0.6666 - accuracy: 0.7886 - val_loss: 2.5187 - val_accuracy: 0.4767\n",
      "epochs: 554\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 530us/step - loss: 0.6664 - accuracy: 0.7914 - val_loss: 2.5387 - val_accuracy: 0.4633\n",
      "epochs: 555\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - ETA: 0s - loss: 0.6606 - accuracy: 0.80 - 0s 479us/step - loss: 0.6626 - accuracy: 0.7900 - val_loss: 2.5278 - val_accuracy: 0.4667\n",
      "epochs: 556\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6652 - accuracy: 0.7957 - val_loss: 2.5356 - val_accuracy: 0.4467\n",
      "epochs: 557\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.6638 - accuracy: 0.7943 - val_loss: 2.5679 - val_accuracy: 0.4667\n",
      "epochs: 558\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6651 - accuracy: 0.7943 - val_loss: 2.5464 - val_accuracy: 0.4467\n",
      "epochs: 559\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 440us/step - loss: 0.6635 - accuracy: 0.7971 - val_loss: 2.5292 - val_accuracy: 0.4533\n",
      "epochs: 560\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6635 - accuracy: 0.7871 - val_loss: 2.5431 - val_accuracy: 0.4700\n",
      "epochs: 561\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6639 - accuracy: 0.7957 - val_loss: 2.5573 - val_accuracy: 0.4600\n",
      "epochs: 562\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6635 - accuracy: 0.7943 - val_loss: 2.5424 - val_accuracy: 0.4633\n",
      "epochs: 563\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 440us/step - loss: 0.6640 - accuracy: 0.7943 - val_loss: 2.5345 - val_accuracy: 0.4633\n",
      "epochs: 564\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 441us/step - loss: 0.6621 - accuracy: 0.7943 - val_loss: 2.5577 - val_accuracy: 0.4467\n",
      "epochs: 565\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 433us/step - loss: 0.6629 - accuracy: 0.7914 - val_loss: 2.5475 - val_accuracy: 0.4667\n",
      "epochs: 566\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6613 - accuracy: 0.7929 - val_loss: 2.5551 - val_accuracy: 0.4700\n",
      "epochs: 567\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.6609 - accuracy: 0.7943 - val_loss: 2.5712 - val_accuracy: 0.4633\n",
      "epochs: 568\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 450us/step - loss: 0.6596 - accuracy: 0.7943 - val_loss: 2.5561 - val_accuracy: 0.4633\n",
      "epochs: 569\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 445us/step - loss: 0.6607 - accuracy: 0.7900 - val_loss: 2.5771 - val_accuracy: 0.4667\n",
      "epochs: 570\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.6596 - accuracy: 0.7957 - val_loss: 2.5688 - val_accuracy: 0.4633\n",
      "epochs: 571\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 454us/step - loss: 0.6583 - accuracy: 0.7929 - val_loss: 2.5836 - val_accuracy: 0.4667\n",
      "epochs: 572\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.6591 - accuracy: 0.8014 - val_loss: 2.5742 - val_accuracy: 0.4667\n",
      "epochs: 573\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 455us/step - loss: 0.6591 - accuracy: 0.7943 - val_loss: 2.5640 - val_accuracy: 0.4567\n",
      "epochs: 574\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 466us/step - loss: 0.6594 - accuracy: 0.7986 - val_loss: 2.5689 - val_accuracy: 0.4633\n",
      "epochs: 575\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 458us/step - loss: 0.6586 - accuracy: 0.7943 - val_loss: 2.6027 - val_accuracy: 0.4667\n",
      "epochs: 576\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 450us/step - loss: 0.6591 - accuracy: 0.7986 - val_loss: 2.5601 - val_accuracy: 0.4633\n",
      "epochs: 577\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.6563 - accuracy: 0.7914 - val_loss: 2.5982 - val_accuracy: 0.4700\n",
      "epochs: 578\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.6563 - accuracy: 0.8000 - val_loss: 2.5962 - val_accuracy: 0.4667\n",
      "epochs: 579\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.6572 - accuracy: 0.7943 - val_loss: 2.5934 - val_accuracy: 0.4667\n",
      "epochs: 580\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.6554 - accuracy: 0.7943 - val_loss: 2.5870 - val_accuracy: 0.4500\n",
      "epochs: 581\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 432us/step - loss: 0.6552 - accuracy: 0.7986 - val_loss: 2.6108 - val_accuracy: 0.4633\n",
      "epochs: 582\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 422us/step - loss: 0.6540 - accuracy: 0.7957 - val_loss: 2.5871 - val_accuracy: 0.4633\n",
      "epochs: 583\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6544 - accuracy: 0.7943 - val_loss: 2.5817 - val_accuracy: 0.4500\n",
      "epochs: 584\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 449us/step - loss: 0.6565 - accuracy: 0.7971 - val_loss: 2.5877 - val_accuracy: 0.4600\n",
      "epochs: 585\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6547 - accuracy: 0.7957 - val_loss: 2.6019 - val_accuracy: 0.4500\n",
      "epochs: 586\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 421us/step - loss: 0.6551 - accuracy: 0.7971 - val_loss: 2.5889 - val_accuracy: 0.4633\n",
      "epochs: 587\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 430us/step - loss: 0.6550 - accuracy: 0.7971 - val_loss: 2.6070 - val_accuracy: 0.4633\n",
      "epochs: 588\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 434us/step - loss: 0.6526 - accuracy: 0.8029 - val_loss: 2.6109 - val_accuracy: 0.4633\n",
      "epochs: 589\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 448us/step - loss: 0.6548 - accuracy: 0.8000 - val_loss: 2.5915 - val_accuracy: 0.4600\n",
      "epochs: 590\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 443us/step - loss: 0.6527 - accuracy: 0.7986 - val_loss: 2.6309 - val_accuracy: 0.4567\n",
      "epochs: 591\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 446us/step - loss: 0.6526 - accuracy: 0.8014 - val_loss: 2.6026 - val_accuracy: 0.4533\n",
      "epochs: 592\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 471us/step - loss: 0.6521 - accuracy: 0.8029 - val_loss: 2.6061 - val_accuracy: 0.4567\n",
      "epochs: 593\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6515 - accuracy: 0.8000 - val_loss: 2.6285 - val_accuracy: 0.4500\n",
      "epochs: 594\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 458us/step - loss: 0.6513 - accuracy: 0.7971 - val_loss: 2.6040 - val_accuracy: 0.4467\n",
      "epochs: 595\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.6508 - accuracy: 0.7971 - val_loss: 2.6140 - val_accuracy: 0.4567\n",
      "epochs: 596\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 451us/step - loss: 0.6501 - accuracy: 0.7971 - val_loss: 2.6179 - val_accuracy: 0.4567\n",
      "epochs: 597\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 433us/step - loss: 0.6501 - accuracy: 0.8000 - val_loss: 2.6459 - val_accuracy: 0.4600\n",
      "epochs: 598\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 416us/step - loss: 0.6491 - accuracy: 0.7943 - val_loss: 2.6286 - val_accuracy: 0.4467\n",
      "epochs: 599\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6489 - accuracy: 0.7986 - val_loss: 2.6039 - val_accuracy: 0.4600\n",
      "epochs: 600\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.6503 - accuracy: 0.8000 - val_loss: 2.6234 - val_accuracy: 0.4600\n",
      "epochs: 601\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 531us/step - loss: 0.6511 - accuracy: 0.7929 - val_loss: 2.6056 - val_accuracy: 0.4567\n",
      "epochs: 602\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 528us/step - loss: 0.6487 - accuracy: 0.8029 - val_loss: 2.6274 - val_accuracy: 0.4533\n",
      "epochs: 603\n",
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      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 525us/step - loss: 0.6490 - accuracy: 0.8029 - val_loss: 2.6473 - val_accuracy: 0.4500\n",
      "epochs: 604\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.6480 - accuracy: 0.8057 - val_loss: 2.6145 - val_accuracy: 0.4533\n",
      "epochs: 605\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.6486 - accuracy: 0.8014 - val_loss: 2.6408 - val_accuracy: 0.4500\n",
      "epochs: 606\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.6470 - accuracy: 0.8000 - val_loss: 2.6315 - val_accuracy: 0.4633\n",
      "epochs: 607\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 422us/step - loss: 0.6461 - accuracy: 0.8014 - val_loss: 2.6526 - val_accuracy: 0.4500\n",
      "epochs: 608\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.6448 - accuracy: 0.8043 - val_loss: 2.6289 - val_accuracy: 0.4633\n",
      "epochs: 609\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 452us/step - loss: 0.6448 - accuracy: 0.8029 - val_loss: 2.6191 - val_accuracy: 0.4533\n",
      "epochs: 610\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.6448 - accuracy: 0.8043 - val_loss: 2.6257 - val_accuracy: 0.4567\n",
      "epochs: 611\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.6434 - accuracy: 0.8000 - val_loss: 2.6339 - val_accuracy: 0.4567\n",
      "epochs: 612\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 442us/step - loss: 0.6462 - accuracy: 0.8014 - val_loss: 2.6350 - val_accuracy: 0.4567\n",
      "epochs: 613\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 435us/step - loss: 0.6441 - accuracy: 0.8029 - val_loss: 2.6469 - val_accuracy: 0.4500\n",
      "epochs: 614\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "700/700 [==============================] - 0s 487us/step - loss: 0.6439 - accuracy: 0.8014 - val_loss: 2.6349 - val_accuracy: 0.4600\n",
      "epochs: 615\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 447us/step - loss: 0.6456 - accuracy: 0.8029 - val_loss: 2.6678 - val_accuracy: 0.4600\n",
      "epochs: 616\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 444us/step - loss: 0.6445 - accuracy: 0.8029 - val_loss: 2.6600 - val_accuracy: 0.4600\n",
      "epochs: 617\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6428 - accuracy: 0.8014 - val_loss: 2.6226 - val_accuracy: 0.4433\n",
      "epochs: 618\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 454us/step - loss: 0.6432 - accuracy: 0.8014 - val_loss: 2.6449 - val_accuracy: 0.4600\n",
      "epochs: 619\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 433us/step - loss: 0.6428 - accuracy: 0.7986 - val_loss: 2.6764 - val_accuracy: 0.4467\n",
      "epochs: 620\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 433us/step - loss: 0.6425 - accuracy: 0.8043 - val_loss: 2.6698 - val_accuracy: 0.4467\n",
      "epochs: 621\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6423 - accuracy: 0.8071 - val_loss: 2.6986 - val_accuracy: 0.4600\n",
      "epochs: 622\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 453us/step - loss: 0.6419 - accuracy: 0.8043 - val_loss: 2.6629 - val_accuracy: 0.4633\n",
      "epochs: 623\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 454us/step - loss: 0.6432 - accuracy: 0.8029 - val_loss: 2.6935 - val_accuracy: 0.4600\n",
      "epochs: 624\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6409 - accuracy: 0.8029 - val_loss: 2.6786 - val_accuracy: 0.4567\n",
      "epochs: 625\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 455us/step - loss: 0.6407 - accuracy: 0.8029 - val_loss: 2.6707 - val_accuracy: 0.4500\n",
      "epochs: 626\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.6402 - accuracy: 0.8071 - val_loss: 2.6739 - val_accuracy: 0.4500\n",
      "epochs: 627\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 469us/step - loss: 0.6408 - accuracy: 0.8086 - val_loss: 2.6705 - val_accuracy: 0.4533\n",
      "epochs: 628\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 477us/step - loss: 0.6378 - accuracy: 0.8086 - val_loss: 2.6651 - val_accuracy: 0.4533\n",
      "epochs: 629\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.6389 - accuracy: 0.8029 - val_loss: 2.7116 - val_accuracy: 0.4600\n",
      "epochs: 630\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - ETA: 0s - loss: 0.6389 - accuracy: 0.81 - 0s 475us/step - loss: 0.6385 - accuracy: 0.8100 - val_loss: 2.6800 - val_accuracy: 0.4500\n",
      "epochs: 631\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 475us/step - loss: 0.6383 - accuracy: 0.8000 - val_loss: 2.6959 - val_accuracy: 0.4467\n",
      "epochs: 632\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 475us/step - loss: 0.6375 - accuracy: 0.8043 - val_loss: 2.7083 - val_accuracy: 0.4533\n",
      "epochs: 633\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.6374 - accuracy: 0.8043 - val_loss: 2.6956 - val_accuracy: 0.4433\n",
      "epochs: 634\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 479us/step - loss: 0.6375 - accuracy: 0.8043 - val_loss: 2.6990 - val_accuracy: 0.4467\n",
      "epochs: 635\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.6368 - accuracy: 0.8043 - val_loss: 2.6646 - val_accuracy: 0.4500\n",
      "epochs: 636\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6363 - accuracy: 0.8100 - val_loss: 2.6911 - val_accuracy: 0.4500\n",
      "epochs: 637\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6365 - accuracy: 0.8071 - val_loss: 2.6927 - val_accuracy: 0.4533\n",
      "epochs: 638\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6364 - accuracy: 0.8086 - val_loss: 2.7060 - val_accuracy: 0.4433\n",
      "epochs: 639\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.6374 - accuracy: 0.8029 - val_loss: 2.6976 - val_accuracy: 0.4400\n",
      "epochs: 640\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 469us/step - loss: 0.6373 - accuracy: 0.8057 - val_loss: 2.6891 - val_accuracy: 0.4533\n",
      "epochs: 641\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 483us/step - loss: 0.6361 - accuracy: 0.8071 - val_loss: 2.6936 - val_accuracy: 0.4533\n",
      "epochs: 642\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 514us/step - loss: 0.6353 - accuracy: 0.8086 - val_loss: 2.7259 - val_accuracy: 0.4367\n",
      "epochs: 643\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 493us/step - loss: 0.6348 - accuracy: 0.8086 - val_loss: 2.7112 - val_accuracy: 0.4533\n",
      "epochs: 644\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 479us/step - loss: 0.6341 - accuracy: 0.8086 - val_loss: 2.7189 - val_accuracy: 0.4567\n",
      "epochs: 645\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6333 - accuracy: 0.8057 - val_loss: 2.6974 - val_accuracy: 0.4533\n",
      "epochs: 646\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 477us/step - loss: 0.6342 - accuracy: 0.8086 - val_loss: 2.7178 - val_accuracy: 0.4467\n",
      "epochs: 647\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.6344 - accuracy: 0.8100 - val_loss: 2.6898 - val_accuracy: 0.4467\n",
      "epochs: 648\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 503us/step - loss: 0.6334 - accuracy: 0.8086 - val_loss: 2.6988 - val_accuracy: 0.4433\n",
      "epochs: 649\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 543us/step - loss: 0.6333 - accuracy: 0.8129 - val_loss: 2.6942 - val_accuracy: 0.4467\n",
      "epochs: 650\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 563us/step - loss: 0.6329 - accuracy: 0.8086 - val_loss: 2.7154 - val_accuracy: 0.4400\n",
      "epochs: 651\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 592us/step - loss: 0.6319 - accuracy: 0.8043 - val_loss: 2.7156 - val_accuracy: 0.4400\n",
      "epochs: 652\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.6314 - accuracy: 0.8086 - val_loss: 2.7390 - val_accuracy: 0.4433\n",
      "epochs: 653\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 493us/step - loss: 0.6316 - accuracy: 0.8086 - val_loss: 2.7694 - val_accuracy: 0.4533\n",
      "epochs: 654\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.6313 - accuracy: 0.8057 - val_loss: 2.7289 - val_accuracy: 0.4433\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 655\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 469us/step - loss: 0.6306 - accuracy: 0.8071 - val_loss: 2.7485 - val_accuracy: 0.4467\n",
      "epochs: 656\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 506us/step - loss: 0.6309 - accuracy: 0.8114 - val_loss: 2.7318 - val_accuracy: 0.4433\n",
      "epochs: 657\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 456us/step - loss: 0.6315 - accuracy: 0.8086 - val_loss: 2.7463 - val_accuracy: 0.4400\n",
      "epochs: 658\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 476us/step - loss: 0.6311 - accuracy: 0.8057 - val_loss: 2.7227 - val_accuracy: 0.4467\n",
      "epochs: 659\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.6296 - accuracy: 0.8100 - val_loss: 2.7647 - val_accuracy: 0.4467\n",
      "epochs: 660\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 484us/step - loss: 0.6311 - accuracy: 0.8100 - val_loss: 2.7366 - val_accuracy: 0.4400\n",
      "epochs: 661\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6274 - accuracy: 0.8100 - val_loss: 2.7378 - val_accuracy: 0.4500\n",
      "epochs: 662\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 477us/step - loss: 0.6295 - accuracy: 0.8071 - val_loss: 2.7341 - val_accuracy: 0.4533\n",
      "epochs: 663\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6290 - accuracy: 0.8071 - val_loss: 2.7452 - val_accuracy: 0.4467\n",
      "epochs: 664\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.6306 - accuracy: 0.8071 - val_loss: 2.7341 - val_accuracy: 0.4433\n",
      "epochs: 665\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6278 - accuracy: 0.8114 - val_loss: 2.7627 - val_accuracy: 0.4433\n",
      "epochs: 666\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 471us/step - loss: 0.6286 - accuracy: 0.8086 - val_loss: 2.7532 - val_accuracy: 0.4433\n",
      "epochs: 667\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.6266 - accuracy: 0.8114 - val_loss: 2.7425 - val_accuracy: 0.4500\n",
      "epochs: 668\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 482us/step - loss: 0.6266 - accuracy: 0.8129 - val_loss: 2.7613 - val_accuracy: 0.4467\n",
      "epochs: 669\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 487us/step - loss: 0.6255 - accuracy: 0.8129 - val_loss: 2.7627 - val_accuracy: 0.4500\n",
      "epochs: 670\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6276 - accuracy: 0.8114 - val_loss: 2.8001 - val_accuracy: 0.4500\n",
      "epochs: 671\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 459us/step - loss: 0.6250 - accuracy: 0.8100 - val_loss: 2.7455 - val_accuracy: 0.4400\n",
      "epochs: 672\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 511us/step - loss: 0.6267 - accuracy: 0.8100 - val_loss: 2.7748 - val_accuracy: 0.4367\n",
      "epochs: 673\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 471us/step - loss: 0.6262 - accuracy: 0.8086 - val_loss: 2.7495 - val_accuracy: 0.4433\n",
      "epochs: 674\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 483us/step - loss: 0.6252 - accuracy: 0.8114 - val_loss: 2.7649 - val_accuracy: 0.4400\n",
      "epochs: 675\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 482us/step - loss: 0.6262 - accuracy: 0.8114 - val_loss: 2.7395 - val_accuracy: 0.4433\n",
      "epochs: 676\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6249 - accuracy: 0.8100 - val_loss: 2.7657 - val_accuracy: 0.4367\n",
      "epochs: 677\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.6245 - accuracy: 0.8129 - val_loss: 2.7914 - val_accuracy: 0.4367\n",
      "epochs: 678\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.6241 - accuracy: 0.8129 - val_loss: 2.7541 - val_accuracy: 0.4467\n",
      "epochs: 679\n",
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      "700/700 [==============================] - 0s 493us/step - loss: 0.6243 - accuracy: 0.8129 - val_loss: 2.7788 - val_accuracy: 0.4433\n",
      "epochs: 680\n",
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      "700/700 [==============================] - 0s 518us/step - loss: 0.6234 - accuracy: 0.8129 - val_loss: 2.7499 - val_accuracy: 0.4433\n",
      "epochs: 681\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 465us/step - loss: 0.6227 - accuracy: 0.8114 - val_loss: 2.7973 - val_accuracy: 0.4433\n",
      "epochs: 682\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 489us/step - loss: 0.6227 - accuracy: 0.8114 - val_loss: 2.7967 - val_accuracy: 0.4433\n",
      "epochs: 683\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 491us/step - loss: 0.6229 - accuracy: 0.8114 - val_loss: 2.7950 - val_accuracy: 0.4400\n",
      "epochs: 684\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 498us/step - loss: 0.6231 - accuracy: 0.8114 - val_loss: 2.7596 - val_accuracy: 0.4500\n",
      "epochs: 685\n",
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      "700/700 [==============================] - 0s 472us/step - loss: 0.6232 - accuracy: 0.8114 - val_loss: 2.7825 - val_accuracy: 0.4400\n",
      "epochs: 686\n",
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      "700/700 [==============================] - 0s 471us/step - loss: 0.6219 - accuracy: 0.8100 - val_loss: 2.7948 - val_accuracy: 0.4400\n",
      "epochs: 687\n",
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      "700/700 [==============================] - 0s 522us/step - loss: 0.6216 - accuracy: 0.8086 - val_loss: 2.7745 - val_accuracy: 0.4433\n",
      "epochs: 688\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 493us/step - loss: 0.6210 - accuracy: 0.8114 - val_loss: 2.7857 - val_accuracy: 0.4400\n",
      "epochs: 689\n",
      "Train on 700 samples, validate on 300 samples\n",
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      "700/700 [==============================] - 0s 490us/step - loss: 0.6218 - accuracy: 0.8100 - val_loss: 2.7836 - val_accuracy: 0.4433\n",
      "epochs: 690\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.6205 - accuracy: 0.8157 - val_loss: 2.7730 - val_accuracy: 0.4400\n",
      "epochs: 691\n",
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      "700/700 [==============================] - 0s 476us/step - loss: 0.6206 - accuracy: 0.8100 - val_loss: 2.7883 - val_accuracy: 0.4367\n",
      "epochs: 692\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 491us/step - loss: 0.6202 - accuracy: 0.8100 - val_loss: 2.8089 - val_accuracy: 0.4433\n",
      "epochs: 693\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6205 - accuracy: 0.8100 - val_loss: 2.8013 - val_accuracy: 0.4433\n",
      "epochs: 694\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.6193 - accuracy: 0.8114 - val_loss: 2.8125 - val_accuracy: 0.4367\n",
      "epochs: 695\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 563us/step - loss: 0.6193 - accuracy: 0.8114 - val_loss: 2.8076 - val_accuracy: 0.4433\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 696\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 570us/step - loss: 0.6190 - accuracy: 0.8129 - val_loss: 2.8030 - val_accuracy: 0.4433\n",
      "epochs: 697\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 555us/step - loss: 0.6201 - accuracy: 0.8100 - val_loss: 2.7838 - val_accuracy: 0.4400\n",
      "epochs: 698\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.6182 - accuracy: 0.8129 - val_loss: 2.7892 - val_accuracy: 0.4467\n",
      "epochs: 699\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.6194 - accuracy: 0.8129 - val_loss: 2.7803 - val_accuracy: 0.4467\n",
      "epochs: 700\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 467us/step - loss: 0.6179 - accuracy: 0.8143 - val_loss: 2.8199 - val_accuracy: 0.4367\n",
      "epochs: 701\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.6166 - accuracy: 0.8143 - val_loss: 2.7954 - val_accuracy: 0.4467\n",
      "epochs: 702\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 461us/step - loss: 0.6179 - accuracy: 0.8114 - val_loss: 2.7806 - val_accuracy: 0.4433\n",
      "epochs: 703\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.6178 - accuracy: 0.8129 - val_loss: 2.8173 - val_accuracy: 0.4367\n",
      "epochs: 704\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.6166 - accuracy: 0.8100 - val_loss: 2.8246 - val_accuracy: 0.4400\n",
      "epochs: 705\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6153 - accuracy: 0.8129 - val_loss: 2.8101 - val_accuracy: 0.4433\n",
      "epochs: 706\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 457us/step - loss: 0.6178 - accuracy: 0.8143 - val_loss: 2.8206 - val_accuracy: 0.4467\n",
      "epochs: 707\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6158 - accuracy: 0.8143 - val_loss: 2.8201 - val_accuracy: 0.4400\n",
      "epochs: 708\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.6157 - accuracy: 0.8114 - val_loss: 2.8338 - val_accuracy: 0.4400\n",
      "epochs: 709\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 481us/step - loss: 0.6147 - accuracy: 0.8129 - val_loss: 2.8583 - val_accuracy: 0.4400\n",
      "epochs: 710\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 508us/step - loss: 0.6148 - accuracy: 0.8171 - val_loss: 2.8148 - val_accuracy: 0.4433\n",
      "epochs: 711\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 464us/step - loss: 0.6140 - accuracy: 0.8157 - val_loss: 2.8176 - val_accuracy: 0.4500\n",
      "epochs: 712\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 478us/step - loss: 0.6157 - accuracy: 0.8129 - val_loss: 2.8296 - val_accuracy: 0.4500\n",
      "epochs: 713\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.6136 - accuracy: 0.8129 - val_loss: 2.8340 - val_accuracy: 0.4367\n",
      "epochs: 714\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 482us/step - loss: 0.6138 - accuracy: 0.8143 - val_loss: 2.7949 - val_accuracy: 0.4433\n",
      "epochs: 715\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.6149 - accuracy: 0.8200 - val_loss: 2.8191 - val_accuracy: 0.4367\n",
      "epochs: 716\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6141 - accuracy: 0.8157 - val_loss: 2.8688 - val_accuracy: 0.4433\n",
      "epochs: 717\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6143 - accuracy: 0.8100 - val_loss: 2.8513 - val_accuracy: 0.4400\n",
      "epochs: 718\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.6131 - accuracy: 0.8143 - val_loss: 2.8303 - val_accuracy: 0.4467\n",
      "epochs: 719\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.6132 - accuracy: 0.8186 - val_loss: 2.8971 - val_accuracy: 0.4300\n",
      "epochs: 720\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.6135 - accuracy: 0.8129 - val_loss: 2.8653 - val_accuracy: 0.4367\n",
      "epochs: 721\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.6123 - accuracy: 0.8171 - val_loss: 2.8337 - val_accuracy: 0.4400\n",
      "epochs: 722\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 463us/step - loss: 0.6131 - accuracy: 0.8100 - val_loss: 2.8444 - val_accuracy: 0.4400\n",
      "epochs: 723\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 477us/step - loss: 0.6125 - accuracy: 0.8143 - val_loss: 2.8628 - val_accuracy: 0.4433\n",
      "epochs: 724\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 468us/step - loss: 0.6098 - accuracy: 0.8171 - val_loss: 2.9029 - val_accuracy: 0.4367\n",
      "epochs: 725\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.6111 - accuracy: 0.8143 - val_loss: 2.8545 - val_accuracy: 0.4433\n",
      "epochs: 726\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.6113 - accuracy: 0.8171 - val_loss: 2.8530 - val_accuracy: 0.4433\n",
      "epochs: 727\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.6111 - accuracy: 0.8129 - val_loss: 2.8426 - val_accuracy: 0.4400\n",
      "epochs: 728\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 465us/step - loss: 0.6092 - accuracy: 0.8186 - val_loss: 2.8565 - val_accuracy: 0.4367\n",
      "epochs: 729\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 462us/step - loss: 0.6096 - accuracy: 0.8214 - val_loss: 2.8631 - val_accuracy: 0.4467\n",
      "epochs: 730\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 470us/step - loss: 0.6106 - accuracy: 0.8143 - val_loss: 2.8336 - val_accuracy: 0.4400\n",
      "epochs: 731\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.6099 - accuracy: 0.8171 - val_loss: 2.8458 - val_accuracy: 0.4433\n",
      "epochs: 732\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 472us/step - loss: 0.6108 - accuracy: 0.8143 - val_loss: 2.8735 - val_accuracy: 0.4467\n",
      "epochs: 733\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.6084 - accuracy: 0.8186 - val_loss: 2.8514 - val_accuracy: 0.4433\n",
      "epochs: 734\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 479us/step - loss: 0.6080 - accuracy: 0.8157 - val_loss: 2.8490 - val_accuracy: 0.4400\n",
      "epochs: 735\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 501us/step - loss: 0.6091 - accuracy: 0.8114 - val_loss: 2.8535 - val_accuracy: 0.4400\n",
      "epochs: 736\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 508us/step - loss: 0.6083 - accuracy: 0.8200 - val_loss: 2.8851 - val_accuracy: 0.4433\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 737\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 549us/step - loss: 0.6075 - accuracy: 0.8143 - val_loss: 2.8585 - val_accuracy: 0.4400\n",
      "epochs: 738\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 491us/step - loss: 0.6073 - accuracy: 0.8157 - val_loss: 2.8741 - val_accuracy: 0.4400\n",
      "epochs: 739\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.6087 - accuracy: 0.8214 - val_loss: 2.8510 - val_accuracy: 0.4400\n",
      "epochs: 740\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.6078 - accuracy: 0.8186 - val_loss: 2.8652 - val_accuracy: 0.4433\n",
      "epochs: 741\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 568us/step - loss: 0.6069 - accuracy: 0.8200 - val_loss: 2.8696 - val_accuracy: 0.4400\n",
      "epochs: 742\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 609us/step - loss: 0.6062 - accuracy: 0.8171 - val_loss: 2.8706 - val_accuracy: 0.4433\n",
      "epochs: 743\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 576us/step - loss: 0.6082 - accuracy: 0.8186 - val_loss: 2.8783 - val_accuracy: 0.4433\n",
      "epochs: 744\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.6065 - accuracy: 0.8229 - val_loss: 2.8797 - val_accuracy: 0.4433\n",
      "epochs: 745\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 491us/step - loss: 0.6044 - accuracy: 0.8200 - val_loss: 2.8910 - val_accuracy: 0.4467\n",
      "epochs: 746\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 501us/step - loss: 0.6064 - accuracy: 0.8157 - val_loss: 2.8753 - val_accuracy: 0.4467\n",
      "epochs: 747\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.6068 - accuracy: 0.8171 - val_loss: 2.8780 - val_accuracy: 0.4433\n",
      "epochs: 748\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.6040 - accuracy: 0.8214 - val_loss: 2.8854 - val_accuracy: 0.4433\n",
      "epochs: 749\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 517us/step - loss: 0.6055 - accuracy: 0.8186 - val_loss: 2.8809 - val_accuracy: 0.4367\n",
      "epochs: 750\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 526us/step - loss: 0.6044 - accuracy: 0.8157 - val_loss: 2.9092 - val_accuracy: 0.4433\n",
      "epochs: 751\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 531us/step - loss: 0.6045 - accuracy: 0.8186 - val_loss: 2.8691 - val_accuracy: 0.4433\n",
      "epochs: 752\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.6049 - accuracy: 0.8200 - val_loss: 2.9086 - val_accuracy: 0.4400\n",
      "epochs: 753\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 528us/step - loss: 0.6034 - accuracy: 0.8214 - val_loss: 2.8982 - val_accuracy: 0.4400\n",
      "epochs: 754\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 508us/step - loss: 0.6040 - accuracy: 0.8229 - val_loss: 2.9040 - val_accuracy: 0.4400\n",
      "epochs: 755\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 502us/step - loss: 0.6031 - accuracy: 0.8186 - val_loss: 2.9013 - val_accuracy: 0.4467\n",
      "epochs: 756\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 524us/step - loss: 0.6031 - accuracy: 0.8200 - val_loss: 2.9107 - val_accuracy: 0.4400\n",
      "epochs: 757\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 526us/step - loss: 0.6025 - accuracy: 0.8214 - val_loss: 2.8973 - val_accuracy: 0.4367\n",
      "epochs: 758\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 529us/step - loss: 0.6032 - accuracy: 0.8200 - val_loss: 2.9201 - val_accuracy: 0.4433\n",
      "epochs: 759\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.6026 - accuracy: 0.8186 - val_loss: 2.9205 - val_accuracy: 0.4400\n",
      "epochs: 760\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.6013 - accuracy: 0.8229 - val_loss: 2.8911 - val_accuracy: 0.4467\n",
      "epochs: 761\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 531us/step - loss: 0.6016 - accuracy: 0.8157 - val_loss: 2.9173 - val_accuracy: 0.4400\n",
      "epochs: 762\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 479us/step - loss: 0.6023 - accuracy: 0.8200 - val_loss: 2.8895 - val_accuracy: 0.4467\n",
      "epochs: 763\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 527us/step - loss: 0.6008 - accuracy: 0.8186 - val_loss: 2.9346 - val_accuracy: 0.4400\n",
      "epochs: 764\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.6001 - accuracy: 0.8214 - val_loss: 2.9176 - val_accuracy: 0.4300\n",
      "epochs: 765\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 537us/step - loss: 0.6009 - accuracy: 0.8243 - val_loss: 2.8936 - val_accuracy: 0.4400\n",
      "epochs: 766\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.6010 - accuracy: 0.8214 - val_loss: 2.8968 - val_accuracy: 0.4433\n",
      "epochs: 767\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 544us/step - loss: 0.6024 - accuracy: 0.8200 - val_loss: 2.9181 - val_accuracy: 0.4367\n",
      "epochs: 768\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 503us/step - loss: 0.6010 - accuracy: 0.8257 - val_loss: 2.9304 - val_accuracy: 0.4400\n",
      "epochs: 769\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 489us/step - loss: 0.5996 - accuracy: 0.8229 - val_loss: 2.9121 - val_accuracy: 0.4400\n",
      "epochs: 770\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.6012 - accuracy: 0.8200 - val_loss: 2.9219 - val_accuracy: 0.4400\n",
      "epochs: 771\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.5988 - accuracy: 0.8257 - val_loss: 2.9262 - val_accuracy: 0.4433\n",
      "epochs: 772\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 486us/step - loss: 0.6001 - accuracy: 0.8214 - val_loss: 2.9252 - val_accuracy: 0.4500\n",
      "epochs: 773\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.5991 - accuracy: 0.8229 - val_loss: 2.9457 - val_accuracy: 0.4400\n",
      "epochs: 774\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.5992 - accuracy: 0.8214 - val_loss: 2.9163 - val_accuracy: 0.4467\n",
      "epochs: 775\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 476us/step - loss: 0.5977 - accuracy: 0.8214 - val_loss: 2.9347 - val_accuracy: 0.4467\n",
      "epochs: 776\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.5980 - accuracy: 0.8214 - val_loss: 2.9403 - val_accuracy: 0.4400\n",
      "epochs: 777\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 490us/step - loss: 0.5987 - accuracy: 0.8229 - val_loss: 2.9443 - val_accuracy: 0.4500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 778\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 486us/step - loss: 0.5987 - accuracy: 0.8229 - val_loss: 2.9304 - val_accuracy: 0.4500\n",
      "epochs: 779\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 460us/step - loss: 0.5971 - accuracy: 0.8257 - val_loss: 2.9510 - val_accuracy: 0.4467\n",
      "epochs: 780\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 501us/step - loss: 0.5954 - accuracy: 0.8229 - val_loss: 2.9465 - val_accuracy: 0.4433\n",
      "epochs: 781\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 499us/step - loss: 0.5979 - accuracy: 0.8243 - val_loss: 2.9374 - val_accuracy: 0.4467\n",
      "epochs: 782\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 523us/step - loss: 0.5982 - accuracy: 0.8214 - val_loss: 2.9641 - val_accuracy: 0.4433\n",
      "epochs: 783\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 493us/step - loss: 0.5959 - accuracy: 0.8229 - val_loss: 2.9124 - val_accuracy: 0.4433\n",
      "epochs: 784\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 563us/step - loss: 0.5962 - accuracy: 0.8200 - val_loss: 2.9440 - val_accuracy: 0.4433\n",
      "epochs: 785\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 612us/step - loss: 0.5968 - accuracy: 0.8214 - val_loss: 2.9214 - val_accuracy: 0.4400\n",
      "epochs: 786\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 624us/step - loss: 0.5958 - accuracy: 0.8229 - val_loss: 2.9478 - val_accuracy: 0.4433\n",
      "epochs: 787\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 562us/step - loss: 0.5955 - accuracy: 0.8243 - val_loss: 2.9615 - val_accuracy: 0.4500\n",
      "epochs: 788\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 540us/step - loss: 0.5947 - accuracy: 0.8257 - val_loss: 2.9143 - val_accuracy: 0.4400\n",
      "epochs: 789\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5940 - accuracy: 0.8229 - val_loss: 2.9278 - val_accuracy: 0.4467\n",
      "epochs: 790\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 474us/step - loss: 0.5936 - accuracy: 0.8243 - val_loss: 2.9552 - val_accuracy: 0.4433\n",
      "epochs: 791\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.5937 - accuracy: 0.8229 - val_loss: 2.9583 - val_accuracy: 0.4433\n",
      "epochs: 792\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 548us/step - loss: 0.5921 - accuracy: 0.8214 - val_loss: 2.9428 - val_accuracy: 0.4400\n",
      "epochs: 793\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 511us/step - loss: 0.5922 - accuracy: 0.8214 - val_loss: 2.9444 - val_accuracy: 0.4500\n",
      "epochs: 794\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 484us/step - loss: 0.5929 - accuracy: 0.8257 - val_loss: 2.9280 - val_accuracy: 0.4400\n",
      "epochs: 795\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 517us/step - loss: 0.5926 - accuracy: 0.8243 - val_loss: 2.9526 - val_accuracy: 0.4400\n",
      "epochs: 796\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 505us/step - loss: 0.5924 - accuracy: 0.8257 - val_loss: 2.9775 - val_accuracy: 0.4433\n",
      "epochs: 797\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 501us/step - loss: 0.5925 - accuracy: 0.8243 - val_loss: 2.9349 - val_accuracy: 0.4500\n",
      "epochs: 798\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.5910 - accuracy: 0.8229 - val_loss: 2.9702 - val_accuracy: 0.4467\n",
      "epochs: 799\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 472us/step - loss: 0.5913 - accuracy: 0.8229 - val_loss: 2.9430 - val_accuracy: 0.4500\n",
      "epochs: 800\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 480us/step - loss: 0.5911 - accuracy: 0.8243 - val_loss: 2.9607 - val_accuracy: 0.4467\n",
      "epochs: 801\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 475us/step - loss: 0.5905 - accuracy: 0.8257 - val_loss: 2.9761 - val_accuracy: 0.4433\n",
      "epochs: 802\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 459us/step - loss: 0.5896 - accuracy: 0.8271 - val_loss: 2.9780 - val_accuracy: 0.4400\n",
      "epochs: 803\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.5896 - accuracy: 0.8271 - val_loss: 2.9568 - val_accuracy: 0.4500\n",
      "epochs: 804\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 489us/step - loss: 0.5899 - accuracy: 0.8243 - val_loss: 2.9800 - val_accuracy: 0.4433\n",
      "epochs: 805\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.5900 - accuracy: 0.8229 - val_loss: 2.9567 - val_accuracy: 0.4467\n",
      "epochs: 806\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.5892 - accuracy: 0.8257 - val_loss: 2.9606 - val_accuracy: 0.4433\n",
      "epochs: 807\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 506us/step - loss: 0.5894 - accuracy: 0.8271 - val_loss: 2.9502 - val_accuracy: 0.4467\n",
      "epochs: 808\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 504us/step - loss: 0.5886 - accuracy: 0.8300 - val_loss: 2.9546 - val_accuracy: 0.4400\n",
      "epochs: 809\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 516us/step - loss: 0.5894 - accuracy: 0.8257 - val_loss: 2.9677 - val_accuracy: 0.4500\n",
      "epochs: 810\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 552us/step - loss: 0.5875 - accuracy: 0.8243 - val_loss: 2.9570 - val_accuracy: 0.4467\n",
      "epochs: 811\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 479us/step - loss: 0.5873 - accuracy: 0.8243 - val_loss: 2.9719 - val_accuracy: 0.4367\n",
      "epochs: 812\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 484us/step - loss: 0.5885 - accuracy: 0.8300 - val_loss: 2.9705 - val_accuracy: 0.4467\n",
      "epochs: 813\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.5872 - accuracy: 0.8257 - val_loss: 2.9705 - val_accuracy: 0.4467\n",
      "epochs: 814\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.5869 - accuracy: 0.8271 - val_loss: 2.9759 - val_accuracy: 0.4467\n",
      "epochs: 815\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 554us/step - loss: 0.5872 - accuracy: 0.8271 - val_loss: 2.9672 - val_accuracy: 0.4467\n",
      "epochs: 816\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 524us/step - loss: 0.5860 - accuracy: 0.8271 - val_loss: 2.9914 - val_accuracy: 0.4500\n",
      "epochs: 817\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 500us/step - loss: 0.5859 - accuracy: 0.8271 - val_loss: 2.9764 - val_accuracy: 0.4400\n",
      "epochs: 818\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.5870 - accuracy: 0.8257 - val_loss: 3.0036 - val_accuracy: 0.4400\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 819\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 493us/step - loss: 0.5861 - accuracy: 0.8271 - val_loss: 3.0033 - val_accuracy: 0.4467\n",
      "epochs: 820\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 537us/step - loss: 0.5863 - accuracy: 0.8257 - val_loss: 2.9972 - val_accuracy: 0.4467\n",
      "epochs: 821\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.5854 - accuracy: 0.8300 - val_loss: 2.9971 - val_accuracy: 0.4433\n",
      "epochs: 822\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 523us/step - loss: 0.5857 - accuracy: 0.8257 - val_loss: 2.9962 - val_accuracy: 0.4467\n",
      "epochs: 823\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 493us/step - loss: 0.5849 - accuracy: 0.8257 - val_loss: 2.9835 - val_accuracy: 0.4567\n",
      "epochs: 824\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 497us/step - loss: 0.5846 - accuracy: 0.8300 - val_loss: 2.9849 - val_accuracy: 0.4333\n",
      "epochs: 825\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.5849 - accuracy: 0.8271 - val_loss: 2.9599 - val_accuracy: 0.4467\n",
      "epochs: 826\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 507us/step - loss: 0.5847 - accuracy: 0.8271 - val_loss: 3.0006 - val_accuracy: 0.4433\n",
      "epochs: 827\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 507us/step - loss: 0.5836 - accuracy: 0.8257 - val_loss: 3.0010 - val_accuracy: 0.4500\n",
      "epochs: 828\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 610us/step - loss: 0.5844 - accuracy: 0.8271 - val_loss: 3.0036 - val_accuracy: 0.4500\n",
      "epochs: 829\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 623us/step - loss: 0.5829 - accuracy: 0.8243 - val_loss: 2.9808 - val_accuracy: 0.4367\n",
      "epochs: 830\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 578us/step - loss: 0.5837 - accuracy: 0.8286 - val_loss: 2.9928 - val_accuracy: 0.4400\n",
      "epochs: 831\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 606us/step - loss: 0.5826 - accuracy: 0.8286 - val_loss: 2.9878 - val_accuracy: 0.4433\n",
      "epochs: 832\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 546us/step - loss: 0.5839 - accuracy: 0.8314 - val_loss: 2.9928 - val_accuracy: 0.4467\n",
      "epochs: 833\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 509us/step - loss: 0.5836 - accuracy: 0.8243 - val_loss: 3.0112 - val_accuracy: 0.4467\n",
      "epochs: 834\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 530us/step - loss: 0.5825 - accuracy: 0.8314 - val_loss: 2.9907 - val_accuracy: 0.4467\n",
      "epochs: 835\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 525us/step - loss: 0.5815 - accuracy: 0.8300 - val_loss: 3.0145 - val_accuracy: 0.4500\n",
      "epochs: 836\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 540us/step - loss: 0.5814 - accuracy: 0.8286 - val_loss: 3.0019 - val_accuracy: 0.4500\n",
      "epochs: 837\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 556us/step - loss: 0.5821 - accuracy: 0.8314 - val_loss: 2.9886 - val_accuracy: 0.4400\n",
      "epochs: 838\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.5820 - accuracy: 0.8286 - val_loss: 3.0151 - val_accuracy: 0.4433\n",
      "epochs: 839\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 543us/step - loss: 0.5813 - accuracy: 0.8300 - val_loss: 3.0107 - val_accuracy: 0.4433\n",
      "epochs: 840\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 526us/step - loss: 0.5812 - accuracy: 0.8300 - val_loss: 3.0139 - val_accuracy: 0.4467\n",
      "epochs: 841\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 506us/step - loss: 0.5794 - accuracy: 0.8271 - val_loss: 3.0280 - val_accuracy: 0.4400\n",
      "epochs: 842\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 591us/step - loss: 0.5809 - accuracy: 0.8271 - val_loss: 3.0102 - val_accuracy: 0.4433\n",
      "epochs: 843\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 563us/step - loss: 0.5821 - accuracy: 0.8257 - val_loss: 3.0196 - val_accuracy: 0.4433\n",
      "epochs: 844\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 542us/step - loss: 0.5790 - accuracy: 0.8314 - val_loss: 3.0120 - val_accuracy: 0.4400\n",
      "epochs: 845\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.5804 - accuracy: 0.8286 - val_loss: 3.0142 - val_accuracy: 0.4467\n",
      "epochs: 846\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 513us/step - loss: 0.5797 - accuracy: 0.8286 - val_loss: 3.0317 - val_accuracy: 0.4400\n",
      "epochs: 847\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 532us/step - loss: 0.5807 - accuracy: 0.8271 - val_loss: 3.0515 - val_accuracy: 0.4500\n",
      "epochs: 848\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 551us/step - loss: 0.5798 - accuracy: 0.8271 - val_loss: 3.0089 - val_accuracy: 0.4433\n",
      "epochs: 849\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 534us/step - loss: 0.5808 - accuracy: 0.8314 - val_loss: 3.0112 - val_accuracy: 0.4433\n",
      "epochs: 850\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 546us/step - loss: 0.5792 - accuracy: 0.8286 - val_loss: 3.0151 - val_accuracy: 0.4433\n",
      "epochs: 851\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 514us/step - loss: 0.5779 - accuracy: 0.8300 - val_loss: 3.0327 - val_accuracy: 0.4400\n",
      "epochs: 852\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 572us/step - loss: 0.5782 - accuracy: 0.8286 - val_loss: 3.0173 - val_accuracy: 0.4467\n",
      "epochs: 853\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 537us/step - loss: 0.5778 - accuracy: 0.8271 - val_loss: 3.0244 - val_accuracy: 0.4367\n",
      "epochs: 854\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 544us/step - loss: 0.5784 - accuracy: 0.8286 - val_loss: 3.0468 - val_accuracy: 0.4467\n",
      "epochs: 855\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 543us/step - loss: 0.5779 - accuracy: 0.8314 - val_loss: 3.0474 - val_accuracy: 0.4433\n",
      "epochs: 856\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 540us/step - loss: 0.5797 - accuracy: 0.8286 - val_loss: 3.0104 - val_accuracy: 0.4533\n",
      "epochs: 857\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 518us/step - loss: 0.5765 - accuracy: 0.8314 - val_loss: 3.0007 - val_accuracy: 0.4367\n",
      "epochs: 858\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 542us/step - loss: 0.5774 - accuracy: 0.8314 - val_loss: 3.0418 - val_accuracy: 0.4367\n",
      "epochs: 859\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 519us/step - loss: 0.5773 - accuracy: 0.8314 - val_loss: 3.0568 - val_accuracy: 0.4467\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 860\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 529us/step - loss: 0.5775 - accuracy: 0.8286 - val_loss: 3.0344 - val_accuracy: 0.4367\n",
      "epochs: 861\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 499us/step - loss: 0.5776 - accuracy: 0.8286 - val_loss: 3.0250 - val_accuracy: 0.4333\n",
      "epochs: 862\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 509us/step - loss: 0.5772 - accuracy: 0.8314 - val_loss: 3.0654 - val_accuracy: 0.4367\n",
      "epochs: 863\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 507us/step - loss: 0.5756 - accuracy: 0.8300 - val_loss: 3.0537 - val_accuracy: 0.4400\n",
      "epochs: 864\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 534us/step - loss: 0.5758 - accuracy: 0.8314 - val_loss: 3.0309 - val_accuracy: 0.4533\n",
      "epochs: 865\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 487us/step - loss: 0.5750 - accuracy: 0.8314 - val_loss: 3.0674 - val_accuracy: 0.4500\n",
      "epochs: 866\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 489us/step - loss: 0.5754 - accuracy: 0.8271 - val_loss: 3.0302 - val_accuracy: 0.4467\n",
      "epochs: 867\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 521us/step - loss: 0.5742 - accuracy: 0.8286 - val_loss: 3.0215 - val_accuracy: 0.4467\n",
      "epochs: 868\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 485us/step - loss: 0.5752 - accuracy: 0.8329 - val_loss: 3.0409 - val_accuracy: 0.4433\n",
      "epochs: 869\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 503us/step - loss: 0.5742 - accuracy: 0.8300 - val_loss: 3.0552 - val_accuracy: 0.4467\n",
      "epochs: 870\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 568us/step - loss: 0.5760 - accuracy: 0.8300 - val_loss: 3.0536 - val_accuracy: 0.4467\n",
      "epochs: 871\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 590us/step - loss: 0.5750 - accuracy: 0.8300 - val_loss: 3.0562 - val_accuracy: 0.4400\n",
      "epochs: 872\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 603us/step - loss: 0.5743 - accuracy: 0.8286 - val_loss: 3.0536 - val_accuracy: 0.4467\n",
      "epochs: 873\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 564us/step - loss: 0.5761 - accuracy: 0.8329 - val_loss: 3.0761 - val_accuracy: 0.4500\n",
      "epochs: 874\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 550us/step - loss: 0.5752 - accuracy: 0.8286 - val_loss: 3.0547 - val_accuracy: 0.4433\n",
      "epochs: 875\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.5730 - accuracy: 0.8314 - val_loss: 3.0647 - val_accuracy: 0.4467\n",
      "epochs: 876\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 497us/step - loss: 0.5735 - accuracy: 0.8314 - val_loss: 3.0897 - val_accuracy: 0.4400\n",
      "epochs: 877\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 538us/step - loss: 0.5726 - accuracy: 0.8314 - val_loss: 3.0942 - val_accuracy: 0.4500\n",
      "epochs: 878\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 519us/step - loss: 0.5736 - accuracy: 0.8300 - val_loss: 3.0497 - val_accuracy: 0.4467\n",
      "epochs: 879\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.5733 - accuracy: 0.8314 - val_loss: 3.0635 - val_accuracy: 0.4533\n",
      "epochs: 880\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 536us/step - loss: 0.5717 - accuracy: 0.8300 - val_loss: 3.0956 - val_accuracy: 0.4467\n",
      "epochs: 881\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 536us/step - loss: 0.5716 - accuracy: 0.8286 - val_loss: 3.1027 - val_accuracy: 0.4467\n",
      "epochs: 882\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 495us/step - loss: 0.5722 - accuracy: 0.8314 - val_loss: 3.0499 - val_accuracy: 0.4433\n",
      "epochs: 883\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 499us/step - loss: 0.5713 - accuracy: 0.8314 - val_loss: 3.0909 - val_accuracy: 0.4467\n",
      "epochs: 884\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 535us/step - loss: 0.5716 - accuracy: 0.8329 - val_loss: 3.0953 - val_accuracy: 0.4500\n",
      "epochs: 885\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 528us/step - loss: 0.5713 - accuracy: 0.8300 - val_loss: 3.0775 - val_accuracy: 0.4500\n",
      "epochs: 886\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 547us/step - loss: 0.5707 - accuracy: 0.8314 - val_loss: 3.0816 - val_accuracy: 0.4367\n",
      "epochs: 887\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.5712 - accuracy: 0.8300 - val_loss: 3.0692 - val_accuracy: 0.4400\n",
      "epochs: 888\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 511us/step - loss: 0.5724 - accuracy: 0.8314 - val_loss: 3.0836 - val_accuracy: 0.4367\n",
      "epochs: 889\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5697 - accuracy: 0.8300 - val_loss: 3.0750 - val_accuracy: 0.4433\n",
      "epochs: 890\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 507us/step - loss: 0.5702 - accuracy: 0.8357 - val_loss: 3.0811 - val_accuracy: 0.4433\n",
      "epochs: 891\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5703 - accuracy: 0.8314 - val_loss: 3.0963 - val_accuracy: 0.4367\n",
      "epochs: 892\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 494us/step - loss: 0.5698 - accuracy: 0.8329 - val_loss: 3.0759 - val_accuracy: 0.4400\n",
      "epochs: 893\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 534us/step - loss: 0.5700 - accuracy: 0.8314 - val_loss: 3.0817 - val_accuracy: 0.4467\n",
      "epochs: 894\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 556us/step - loss: 0.5704 - accuracy: 0.8300 - val_loss: 3.1080 - val_accuracy: 0.4467\n",
      "epochs: 895\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 531us/step - loss: 0.5698 - accuracy: 0.8286 - val_loss: 3.1268 - val_accuracy: 0.4400\n",
      "epochs: 896\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 533us/step - loss: 0.5704 - accuracy: 0.8300 - val_loss: 3.1061 - val_accuracy: 0.4467\n",
      "epochs: 897\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 556us/step - loss: 0.5693 - accuracy: 0.8329 - val_loss: 3.1062 - val_accuracy: 0.4400\n",
      "epochs: 898\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5695 - accuracy: 0.8329 - val_loss: 3.1109 - val_accuracy: 0.4500\n",
      "epochs: 899\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 530us/step - loss: 0.5694 - accuracy: 0.8314 - val_loss: 3.1148 - val_accuracy: 0.4433\n",
      "epochs: 900\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 574us/step - loss: 0.5675 - accuracy: 0.8343 - val_loss: 3.1316 - val_accuracy: 0.4500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epochs: 901\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 560us/step - loss: 0.5670 - accuracy: 0.8314 - val_loss: 3.1242 - val_accuracy: 0.4467\n",
      "epochs: 902\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 560us/step - loss: 0.5691 - accuracy: 0.8343 - val_loss: 3.1175 - val_accuracy: 0.4400\n",
      "epochs: 903\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 513us/step - loss: 0.5671 - accuracy: 0.8343 - val_loss: 3.1140 - val_accuracy: 0.4533\n",
      "epochs: 904\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 548us/step - loss: 0.5657 - accuracy: 0.8343 - val_loss: 3.1000 - val_accuracy: 0.4533\n",
      "epochs: 905\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 523us/step - loss: 0.5677 - accuracy: 0.8343 - val_loss: 3.1094 - val_accuracy: 0.4400\n",
      "epochs: 906\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 516us/step - loss: 0.5669 - accuracy: 0.8343 - val_loss: 3.1202 - val_accuracy: 0.4467\n",
      "epochs: 907\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 559us/step - loss: 0.5660 - accuracy: 0.8343 - val_loss: 3.1436 - val_accuracy: 0.4367\n",
      "epochs: 908\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 553us/step - loss: 0.5665 - accuracy: 0.8329 - val_loss: 3.1420 - val_accuracy: 0.4500\n",
      "epochs: 909\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 532us/step - loss: 0.5663 - accuracy: 0.8314 - val_loss: 3.1502 - val_accuracy: 0.4500\n",
      "epochs: 910\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 534us/step - loss: 0.5663 - accuracy: 0.8357 - val_loss: 3.1230 - val_accuracy: 0.4433\n",
      "epochs: 911\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 538us/step - loss: 0.5657 - accuracy: 0.8343 - val_loss: 3.1649 - val_accuracy: 0.4433\n",
      "epochs: 912\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 580us/step - loss: 0.5657 - accuracy: 0.8329 - val_loss: 3.1417 - val_accuracy: 0.4467\n",
      "epochs: 913\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 631us/step - loss: 0.5641 - accuracy: 0.8329 - val_loss: 3.1385 - val_accuracy: 0.4500\n",
      "epochs: 914\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 615us/step - loss: 0.5639 - accuracy: 0.8329 - val_loss: 3.1163 - val_accuracy: 0.4367\n",
      "epochs: 915\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 601us/step - loss: 0.5652 - accuracy: 0.8357 - val_loss: 3.1110 - val_accuracy: 0.4300\n",
      "epochs: 916\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 556us/step - loss: 0.5644 - accuracy: 0.8343 - val_loss: 3.1260 - val_accuracy: 0.4433\n",
      "epochs: 917\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 542us/step - loss: 0.5630 - accuracy: 0.8329 - val_loss: 3.1292 - val_accuracy: 0.4433\n",
      "epochs: 918\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 550us/step - loss: 0.5634 - accuracy: 0.8343 - val_loss: 3.1372 - val_accuracy: 0.4500\n",
      "epochs: 919\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 558us/step - loss: 0.5642 - accuracy: 0.8357 - val_loss: 3.1527 - val_accuracy: 0.4500\n",
      "epochs: 920\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 539us/step - loss: 0.5638 - accuracy: 0.8343 - val_loss: 3.1502 - val_accuracy: 0.4400\n",
      "epochs: 921\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 613us/step - loss: 0.5626 - accuracy: 0.8343 - val_loss: 3.1604 - val_accuracy: 0.4333\n",
      "epochs: 922\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 616us/step - loss: 0.5628 - accuracy: 0.8343 - val_loss: 3.1642 - val_accuracy: 0.4500\n",
      "epochs: 923\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 559us/step - loss: 0.5626 - accuracy: 0.8343 - val_loss: 3.1682 - val_accuracy: 0.4500\n",
      "epochs: 924\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 1s 726us/step - loss: 0.5622 - accuracy: 0.8357 - val_loss: 3.1357 - val_accuracy: 0.4367\n",
      "epochs: 925\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 643us/step - loss: 0.5629 - accuracy: 0.8314 - val_loss: 3.1562 - val_accuracy: 0.4500\n",
      "epochs: 926\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 567us/step - loss: 0.5626 - accuracy: 0.8343 - val_loss: 3.1476 - val_accuracy: 0.4500\n",
      "epochs: 927\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 554us/step - loss: 0.5624 - accuracy: 0.8357 - val_loss: 3.1481 - val_accuracy: 0.4367\n",
      "epochs: 928\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 598us/step - loss: 0.5608 - accuracy: 0.8343 - val_loss: 3.1665 - val_accuracy: 0.4333\n",
      "epochs: 929\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 636us/step - loss: 0.5632 - accuracy: 0.8343 - val_loss: 3.1672 - val_accuracy: 0.4467\n",
      "epochs: 930\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 547us/step - loss: 0.5617 - accuracy: 0.8357 - val_loss: 3.1504 - val_accuracy: 0.4467\n",
      "epochs: 931\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 557us/step - loss: 0.5610 - accuracy: 0.8343 - val_loss: 3.1656 - val_accuracy: 0.4400\n",
      "epochs: 932\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 607us/step - loss: 0.5606 - accuracy: 0.8343 - val_loss: 3.1741 - val_accuracy: 0.4467\n",
      "epochs: 933\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 669us/step - loss: 0.5607 - accuracy: 0.8357 - val_loss: 3.1568 - val_accuracy: 0.4467\n",
      "epochs: 934\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 614us/step - loss: 0.5607 - accuracy: 0.8314 - val_loss: 3.1639 - val_accuracy: 0.4533\n",
      "epochs: 935\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 527us/step - loss: 0.5597 - accuracy: 0.8343 - val_loss: 3.1758 - val_accuracy: 0.4400\n",
      "epochs: 936\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 538us/step - loss: 0.5601 - accuracy: 0.8343 - val_loss: 3.1815 - val_accuracy: 0.4533\n",
      "epochs: 937\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 589us/step - loss: 0.5595 - accuracy: 0.8357 - val_loss: 3.1796 - val_accuracy: 0.4400\n",
      "epochs: 938\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 502us/step - loss: 0.5597 - accuracy: 0.8343 - val_loss: 3.1460 - val_accuracy: 0.4267\n",
      "epochs: 939\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 525us/step - loss: 0.5593 - accuracy: 0.8357 - val_loss: 3.1770 - val_accuracy: 0.4367\n",
      "epochs: 940\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 525us/step - loss: 0.5591 - accuracy: 0.8357 - val_loss: 3.1584 - val_accuracy: 0.4533\n",
      "epochs: 941\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 506us/step - loss: 0.5594 - accuracy: 0.8357 - val_loss: 3.1792 - val_accuracy: 0.4433\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 942\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.5586 - accuracy: 0.8371 - val_loss: 3.1800 - val_accuracy: 0.4467\n",
      "epochs: 943\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 514us/step - loss: 0.5604 - accuracy: 0.8357 - val_loss: 3.1796 - val_accuracy: 0.4367\n",
      "epochs: 944\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 496us/step - loss: 0.5584 - accuracy: 0.8343 - val_loss: 3.1843 - val_accuracy: 0.4300\n",
      "epochs: 945\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 540us/step - loss: 0.5587 - accuracy: 0.8343 - val_loss: 3.1773 - val_accuracy: 0.4467\n",
      "epochs: 946\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 503us/step - loss: 0.5597 - accuracy: 0.8371 - val_loss: 3.1730 - val_accuracy: 0.4467\n",
      "epochs: 947\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5583 - accuracy: 0.8357 - val_loss: 3.2051 - val_accuracy: 0.4467\n",
      "epochs: 948\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 538us/step - loss: 0.5586 - accuracy: 0.8343 - val_loss: 3.1870 - val_accuracy: 0.4500\n",
      "epochs: 949\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.5584 - accuracy: 0.8357 - val_loss: 3.1770 - val_accuracy: 0.4367\n",
      "epochs: 950\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 506us/step - loss: 0.5580 - accuracy: 0.8343 - val_loss: 3.1992 - val_accuracy: 0.4467\n",
      "epochs: 951\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 504us/step - loss: 0.5571 - accuracy: 0.8343 - val_loss: 3.1987 - val_accuracy: 0.4500\n",
      "epochs: 952\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 554us/step - loss: 0.5573 - accuracy: 0.8371 - val_loss: 3.2150 - val_accuracy: 0.4433\n",
      "epochs: 953\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 584us/step - loss: 0.5578 - accuracy: 0.8343 - val_loss: 3.1721 - val_accuracy: 0.4400\n",
      "epochs: 954\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 620us/step - loss: 0.5578 - accuracy: 0.8343 - val_loss: 3.2146 - val_accuracy: 0.4500\n",
      "epochs: 955\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 581us/step - loss: 0.5565 - accuracy: 0.8343 - val_loss: 3.1748 - val_accuracy: 0.4433\n",
      "epochs: 956\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 519us/step - loss: 0.5577 - accuracy: 0.8343 - val_loss: 3.2032 - val_accuracy: 0.4467\n",
      "epochs: 957\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 492us/step - loss: 0.5566 - accuracy: 0.8343 - val_loss: 3.1903 - val_accuracy: 0.4467\n",
      "epochs: 958\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 517us/step - loss: 0.5568 - accuracy: 0.8343 - val_loss: 3.2146 - val_accuracy: 0.4333\n",
      "epochs: 959\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 536us/step - loss: 0.5561 - accuracy: 0.8371 - val_loss: 3.2178 - val_accuracy: 0.4433\n",
      "epochs: 960\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 523us/step - loss: 0.5563 - accuracy: 0.8357 - val_loss: 3.2145 - val_accuracy: 0.4433\n",
      "epochs: 961\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 509us/step - loss: 0.5554 - accuracy: 0.8371 - val_loss: 3.2168 - val_accuracy: 0.4433\n",
      "epochs: 962\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 529us/step - loss: 0.5558 - accuracy: 0.8357 - val_loss: 3.1926 - val_accuracy: 0.4200\n",
      "epochs: 963\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5568 - accuracy: 0.8357 - val_loss: 3.2044 - val_accuracy: 0.4533\n",
      "epochs: 964\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 510us/step - loss: 0.5553 - accuracy: 0.8343 - val_loss: 3.2102 - val_accuracy: 0.4467\n",
      "epochs: 965\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 504us/step - loss: 0.5543 - accuracy: 0.8357 - val_loss: 3.1991 - val_accuracy: 0.4333\n",
      "epochs: 966\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 534us/step - loss: 0.5544 - accuracy: 0.8357 - val_loss: 3.2129 - val_accuracy: 0.4433\n",
      "epochs: 967\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 502us/step - loss: 0.5540 - accuracy: 0.8371 - val_loss: 3.2230 - val_accuracy: 0.4433\n",
      "epochs: 968\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 477us/step - loss: 0.5574 - accuracy: 0.8314 - val_loss: 3.2029 - val_accuracy: 0.4367\n",
      "epochs: 969\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 520us/step - loss: 0.5544 - accuracy: 0.8343 - val_loss: 3.1697 - val_accuracy: 0.4467\n",
      "epochs: 970\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 531us/step - loss: 0.5551 - accuracy: 0.8371 - val_loss: 3.2313 - val_accuracy: 0.4400\n",
      "epochs: 971\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 528us/step - loss: 0.5545 - accuracy: 0.8357 - val_loss: 3.2340 - val_accuracy: 0.4467\n",
      "epochs: 972\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 504us/step - loss: 0.5543 - accuracy: 0.8371 - val_loss: 3.2210 - val_accuracy: 0.4400\n",
      "epochs: 973\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 526us/step - loss: 0.5535 - accuracy: 0.8357 - val_loss: 3.2465 - val_accuracy: 0.4367\n",
      "epochs: 974\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 514us/step - loss: 0.5558 - accuracy: 0.8357 - val_loss: 3.2411 - val_accuracy: 0.4467\n",
      "epochs: 975\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 524us/step - loss: 0.5534 - accuracy: 0.8343 - val_loss: 3.2236 - val_accuracy: 0.4433\n",
      "epochs: 976\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 511us/step - loss: 0.5533 - accuracy: 0.8343 - val_loss: 3.2424 - val_accuracy: 0.4400\n",
      "epochs: 977\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 499us/step - loss: 0.5531 - accuracy: 0.8343 - val_loss: 3.2185 - val_accuracy: 0.4367\n",
      "epochs: 978\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 528us/step - loss: 0.5528 - accuracy: 0.8371 - val_loss: 3.2356 - val_accuracy: 0.4367\n",
      "epochs: 979\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 518us/step - loss: 0.5527 - accuracy: 0.8357 - val_loss: 3.2450 - val_accuracy: 0.4467\n",
      "epochs: 980\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 533us/step - loss: 0.5515 - accuracy: 0.8371 - val_loss: 3.2374 - val_accuracy: 0.4400\n",
      "epochs: 981\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 549us/step - loss: 0.5523 - accuracy: 0.8371 - val_loss: 3.2591 - val_accuracy: 0.4400\n",
      "epochs: 982\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 543us/step - loss: 0.5522 - accuracy: 0.8343 - val_loss: 3.2389 - val_accuracy: 0.4400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epochs: 983\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.5522 - accuracy: 0.8371 - val_loss: 3.2481 - val_accuracy: 0.4400\n",
      "epochs: 984\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 520us/step - loss: 0.5523 - accuracy: 0.8386 - val_loss: 3.2519 - val_accuracy: 0.4433\n",
      "epochs: 985\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 516us/step - loss: 0.5521 - accuracy: 0.8371 - val_loss: 3.2438 - val_accuracy: 0.4333\n",
      "epochs: 986\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 522us/step - loss: 0.5511 - accuracy: 0.8357 - val_loss: 3.2739 - val_accuracy: 0.4500\n",
      "epochs: 987\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 532us/step - loss: 0.5524 - accuracy: 0.8357 - val_loss: 3.2409 - val_accuracy: 0.4467\n",
      "epochs: 988\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 520us/step - loss: 0.5512 - accuracy: 0.8357 - val_loss: 3.2440 - val_accuracy: 0.4367\n",
      "epochs: 989\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 557us/step - loss: 0.5514 - accuracy: 0.8371 - val_loss: 3.2535 - val_accuracy: 0.4400\n",
      "epochs: 990\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 497us/step - loss: 0.5516 - accuracy: 0.8329 - val_loss: 3.2685 - val_accuracy: 0.4467\n",
      "epochs: 991\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5511 - accuracy: 0.8371 - val_loss: 3.2755 - val_accuracy: 0.4467\n",
      "epochs: 992\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 488us/step - loss: 0.5510 - accuracy: 0.8357 - val_loss: 3.2503 - val_accuracy: 0.4367\n",
      "epochs: 993\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 515us/step - loss: 0.5499 - accuracy: 0.8357 - val_loss: 3.2744 - val_accuracy: 0.4433\n",
      "epochs: 994\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 542us/step - loss: 0.5508 - accuracy: 0.8357 - val_loss: 3.2620 - val_accuracy: 0.4433\n",
      "epochs: 995\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 601us/step - loss: 0.5508 - accuracy: 0.8357 - val_loss: 3.2600 - val_accuracy: 0.4467\n",
      "epochs: 996\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 636us/step - loss: 0.5497 - accuracy: 0.8343 - val_loss: 3.2904 - val_accuracy: 0.4467\n",
      "epochs: 997\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 637us/step - loss: 0.5484 - accuracy: 0.8371 - val_loss: 3.2851 - val_accuracy: 0.4467\n",
      "epochs: 998\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 558us/step - loss: 0.5503 - accuracy: 0.8357 - val_loss: 3.2600 - val_accuracy: 0.4433\n",
      "epochs: 999\n",
      "Train on 700 samples, validate on 300 samples\n",
      "Epoch 1/1\n",
      "700/700 [==============================] - 0s 536us/step - loss: 0.5498 - accuracy: 0.8371 - val_loss: 3.2832 - val_accuracy: 0.4467\n"
     ]
    }
   ],
   "source": [
    "custom_hist=CustomHistory()\n",
    "custom_hist.init()\n",
    "for epoch_idx in range(1000):\n",
    "    print('epochs: '+str(epoch_idx))\n",
    "    model.fit(x_train,y_train,epochs=1,batch_size=10,validation_data=(x_val,y_val),callbacks=[custom_hist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,loss_ax=plt.subplots()\n",
    "acc_ax=loss_ax.twinx()\n",
    "loss_ax.plot(custom_hist.train_loss,'y',label='train loss')\n",
    "loss_ax.plot(custom_hist.val_loss,'r',label='val loss')\n",
    "acc_ax.plot(custom_hist.train_acc,'b',label='train acc')\n",
    "acc_ax.plot(custom_hist.val_acc,'g',label='val acc')\n",
    "\n",
    "loss_ax.set_xlabel('epoch')\n",
    "loss_ax.set_ylabel('loss')\n",
    "acc_ax.set_ylabel('accuracy')\n",
    "\n",
    "loss_ax.legend(loc='upper left')\n",
    "acc_ax.legend(loc='lower left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
